New Jersey Institute of Technology Digital Commons @ NJIT
Dissertations Electronic Theses and Dissertations
Fall 1-31-2009
Optimizing container retrieval operation at a port container terminal
Dejan Besenski New Jersey Institute of Technology
Follow this and additional works at: https://digitalcommons.njit.edu/dissertations
Part of the Transportation Engineering Commons
Recommended Citation Besenski, Dejan, "Optimizing container retrieval operation at a port container terminal" (2009). Dissertations. 886. https://digitalcommons.njit.edu/dissertations/886
This Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at Digital Commons @ NJIT. It has been accepted for inclusion in Dissertations by an authorized administrator of Digital Commons @ NJIT. For more information, please contact [email protected].
Copyright Warning & Restrictions
The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material.
Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be “used for any purpose other than private study, scholarship, or research.” If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of “fair use” that user may be liable for copyright infringement,
This institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law.
Please Note: The author retains the copyright while the New Jersey Institute of Technology reserves the right to distribute this thesis or dissertation
Printing note: If you do not wish to print this page, then select “Pages from: first page # to: last page #” on the print dialog screen
The Van Houten library has removed some of the personal information and all signatures from the approval page and biographical sketches of theses and dissertations in order to protect the identity of NJIT graduates and faculty. A S AC
O IMI I G CO AI E E IE A O E A IO A A O CO AI E E MI A y e a ese ski
I e ese ce o ma o c a e ges w e e o s a e s i i g o im o e ei o e a io a s ay com e i i e i e g o a ma ke e issue o o e icie cy a icu a y e i e ige ma ageme o s a e ca ie s is i es iga e i is s u y A ew a oac is ese e a o imi es co ai e
a i g equi me o e a io a is e e icia o e e mi a o e a o i e ms o im o i g
o uc i i y o i i g as e se ice a e uci g o e a i g cos A so e so u io o i es acce a e se ice o ucke s e o e a io a s a egies ese e a e a e o assess e o e a io
om e ucke s a e mi a o e a o s e s ec i e
e esea c is s uc u e i o ou i e e sce a ios a use a assig me ogic a sc e u i g me o o ogy o esc i e e co i uous ime e e a e assig me ocess o s a e ca ie s o ucks I oi g so i o i es e so u io o e ques io a is isse a io oses e assig me mo e s use i a a em o a swe e esea c ques io a e s a e as o ows
• A mo e is e e o e o assig s a e ca ie s o ucks ase o e i s Come
i s Se e ( C S u e is is e ase i e mo e a o e a go i ms wi y o
im o e u o is so u io
• wo i e e a ica io s o e u ga ia A go i m a e use o e s a e
ca ie o uck assig me o em e i s im eme a io o e u ga ia
A go i m oes o co si e uck wai i g ime e seco mo e im eme s e uck io i y u e i o e assig me oce u e • i a y a eu is ic im eme a io o e im ici e ume a io oce u e e e o s
e seque ce o o s assig e o s a e ca ie s a wi mi imi e e o a is a ce a e e y em
e co ce o a a i g e io is i o uce o mo e s w e e e u ga ia A go i m a e eu is ic wi I ege E ume a io a e use e a i g e io is e i e as a ime i e a wi i w ic o e ca i e o ima so u io o e s a e assig me o em e assum io
a y i o uci g a a i g e io a us aki g a a age o k ow uck-co ai e ai s
o i es e e a oca io o s a e ca ie s o ucks is i es iga e a e a swe s o ques io s a is isse a io a esses a e ese e
A a a ysis is ese e a u i i es e k ow e ge o e uck a i a a e o e e mi e e o ima s a e ca ie ee si e a e u a io o e a i g e io e esu s ca e use y
o e mi a ma ageme o e e o a o ima s a e ca ie e oyme s a egy a a i g e io a wi mi imi e e o a cos o o e a io
e amewo k is esig e o a swe ques io s o i e es o o e mi a ma ageme a
o i es iga e e a e-o e wee e cos o o e a io a e se ice o i e o ucks e a a ysis ese s gui e i es o ici g s a egy i a a oi me sys em is im eme e O IMI I G CO AI E E IE A O E A IO A A O CO AI E E MI A
b j n n
A rt t n S b tt d t th lt f r In t t t f hn l n rt l lf ll nt f th r nt f r th r f t r f h l ph n r n p rt t n
Int rd pl n r r r n r n p rt t n
n r 200 Copyright © 2009 by Dejan Besenski ALL RIGHTS RESERVED A O A AGE
O IMI I G CO AI E E IE A O E A IO A A O CO AI E E MI A
e a ese ski
Dr. Lazar N. Spasovic, Dissertation Advisor Date Professor of Civil and Environmental Engineering, NJIT
Dr, Athanassios Bladikas, Committee Member Date Associate Professor of Industrial and Manufacturing Engineering, NJIT
Dr. Steven Hy Chien, Committee Member Date Professor of Civil and Environmental Engineering, NJIT
Dr. Janice R. Daniel, Committee Member Date Associate Professor of Civil and Environmental Engineering, NJIT
Dr. Sanchoy K. Das, Committee Member Date Professor of Industrial and Manufacturing Engineering, NJIT BIOGRAPHICAL SKETCH
Author: Dejan Besenski
Degree: Doctor of Philosophy
Date: January 2009
Undergraduate and Graduate Education:
• Doctor of Philosophy in Transportation Engineering New Jersey Institute of Technology, Newark, NJ, 2009
• Diploma in Transportation Engineering, Belgrade University, Serbia, 2003
Major: Transportation
Presentation and Publication:
Steven Chien, Lazar Spasovic, Kier Opie, Vivek Korikanthimath, Dejan Besenski, "Simulation-Based Analysis for Toll Plazas with Multiple Toll Collection Methods," The 84th TRB Annual Meeting, Washington DC, January 2005
iv is isse a io is e ica e o my ami y ACK OW E GME
I would like to express my gratitude to my advisor, Dr. Lazar N. Spasovic, for his guidance and encouragement throughout the course of the dissertation research. His insight and his ability to never lose sight of the objective helped me to complete this research,
I would also like to thank Dr, Athanassios K. Bladikas, Dr Steven Chien, Dr, Janice Daniel and Dr. Sanchoy Das for serving as members of the dissertation committee.
I am grateful to Dr. Chi Tang for his assistance in developing the heuristic assignment strategy and Branislav Dimitrijevic for his comments that improved this research.
Finally, I would like to thank my mother Vera, my late father Dragoslav, and my sister Ana, for their support. I can hardly find words to express the gratitude for their love and encouragement,
i A E O CO E S
C a e Page
1 INTRODUCTION 1
1.1 The Purpose of the Dissertation 1
1.2 Organization of the Dissertation 2
2 BACKGROUND 3
2.1 Increasing Trends in Global Container Traffic 4
2,2 Economics of Port Operation 6
2.3 Port Investments 8
3 DESCRIPTION OF A PORT CONTAINER TERMINAL OPERATION 9
3,1 Terminal Description 9
3.2 Landside Area Functions 11
3.2.1 Operations at the Gate 11
3.2,2 Truck Service by Yard Equipment 12
3.3 Dockside Area Function 13
4 STRADDLE CARRIER PROBLEM DESCRIPTION 15
4.1 Introduction 15
4.2 The Straddle Carrier to Truck Assignment Problem 16
4.3 Research Motivation 17
4.3.1 Potential for Reduction in Operating Cost and Truck Service Time 17
4.3.2 The Trade-Off Between Operator Cost and Truck Service 18
4.3,3 Drayage Operation 19
4.3.3.1 Service Improvement and Modeling 20
4.3.3.2 Conclusion 21
4.4 Understanding the Notion of Optimality in a Real Time Solution 21
ii A E O CO E S (C nt n d Ch pt r
4.5 Objectives 24
5 LITERATURE REVIEW 25
5,1 Algorithms for Assigning Equipment at a Container Terminal 25
5.2 Container Terminal Operation, Design and Simulation 36
5.3 Assignment Algorithm 37
5.4 Summary 40
6 ASSIGNMENT MODELS FOR STRADDLE CARRIER SCHEDULING PROBLEM ..,, 41
6.1 Objective Function in Assignment Problems 41
6,2 The Straddle Carrier Assignment Models 42
6.2.1 Heuristic I, Application of the First Come First Served (FCFS) Rule 42
6.2.2 Hungarian Algorithm for Straddle Carrier Assignment 43
6,2.2,1 Hungarian Algorithm I - No Limits on Truck Wait Time 44
6,2.2.2 Hungarian Algorithm II — Truck Priority Rule 46
6.2.3 Heuristic with Implicit Enumeration 49
7 THE APPLICATION OF STRADDLE CARRIER ASSIGNMENT MODELS 54
7,1 Case Study 54
7.1.1 Terminal Layout and Data Description 54
7.1.2 Assumptions 56
7,2 Case Study Results 57
7.2.1 First Come First Served 57
7.2.1.1 Discussion of Results 58
iii TABLE OF CONTENTS (Continued) Chapter Page
7 1 e A oma y 1
7 u ga ia Assig me I esu s 1
7 1 iscussio o esu s 1
7 e A oma y emai s
7 3 u ga ia Assig me II esu s
7 Im ici E ume a io eu is ic esu s 7
7 3 Com a iso e wee eu is ics 9 SE SI I I Y O E S A E CA IE EE SI E A E A I G E IO U A IO WI ES EC O E UCK A I A A E 7 1 Ge e a i g e Sam e a a 73
Cos o S a e Ca ie O e a io 7
3 Simu a io A a ysis 7
Im ac o oa Im a a ce 77
5 Eco omies o Sca e i S a e O e a io 79
Im ica io s o Sam i g e ia io
7 Co c usio
9 MA AGEME IM ICA IO S 3
9 1 a e-o e wee e Cos o O e a io a Se ice Qua i y 3
9 Planning Straddle Carrier Operations with Variable Truck Arrival Rates
Throughout the Day 5
9 3 ici g S uc u e o a A oi me Sys em 9
9 Co c usio 91
ix A E O CO E S (C nt n d Ch pt r
10 CONCLUSION AND THE RECOMMENDATIONS FOR FUTURE RESEARCH 93
10.1 Results and Findings 93
10.2 Contributions of the Research 96
10.2.1 Contribution to Assignment Algorithm Application 96
10.2,2 Contribution to Terminal Management Operation 95
10.3 Recommendations for Future Research 97
APPENDIX A RESULTS OF STRADDLE CARRIERS ASSIGNMENTS 99
A,1 Hungarian Algorithm I — No Limits on Truck Waiting Time 99
A.2 Hungarian Algorithm II — With Truck Priority 103
A.3 Heuristic With Implicit Enumeration 106
APPENDIX B RESULTS OF THE EXPERIMENT 109
B.1 Optimal Solution for Different Arrival Rates 109
B,2 Simulation Results for Truck Arrival Rates Ranging from 25 Trucks per Hour 114 to 125 Trucks per Hour B.2,1 Simulation Results Based on Truck Arrival Rate of 25 Trucks per Hour 114
B.2.2 Simulation Results Based on Truck Arrival Rate of 50 Trucks per Hour 116
B,2.3 Simulation Results Based on Truck Arrival Rate of 75 Trucks per Hour 119
B.2,4 Simulation Results Based on Truck Arrival Rate of 100 Trucks per Hour 121
B.2.5 Simulation Results Based on Truck Arrival Rate of 125 Trucks per Hour 123
REFERENCES 125 LIST OF TABLES
Table Page
1 e s i Co ai e o ume a e o o ew Yo k/ ew e sey
a e Ca i a i es me s i e o o ew Yo k a ew e sey om 7- 1
5 1 Co si e a io s i C oosi g Amo g Ma o Sc e u i g ec iques 35
7 1 esu s o e A ica io o C S u e 5
7 O ima So u io om ou eu is ics 7
1 Simu a io Sce a io S uc u e 7
O ima a i g e io S a e Ca ie ee Si e a e o ma ce o i e e A i a a es 7 3 O ima S a e Ca ie ee Si e a a i g e io as a u c io o Im o ucks A i i g a e e mi a 7 C a ge 79
5 A e age Cos e Co ai e 79
9 1 Cos o O e a io a e o ma ce o 1 S a e Ca ie s
9 e mi a O e a i g a s o i e e ime I e a s u i g e Wo k ay 7
9 3 Cos o O e a io a e o ma ce wi I e a - ase O e a i g a s a i e S a e Ca ie ee 9 Cos o O e a io wi I e a - ase O e a i g a s a S a e S a e Ca ie ee 9 A 1 ee S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 99
A ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A 3 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 1
A 5 Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 1
A Eig S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
i IS O A ES (C nt n d bl
A 7 e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A ee S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 3
A 9 ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 3
A 1 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A 11 Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A 1 Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 5
A 13 Eig S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 5
A 1 ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A 15 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
A 1 Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 7
A 17 Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1 7
A 1 Eig S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 1
1 1 ee S a e Ca ie s i O e a io o uck A i a a e o 5 ucks/ ou 1 9
1 Si S a e Ca ie s i O e a io o uck A i a a e o 5 ucks/ ou 11
1 3 Eig S a e Ca ie s i O e a io o uck A i a a e o 75 ucks/ ou 111
1 e S a e Ca ie s i O e a io o uck A i a a e o 1 ucks/ ou 11
1 5 i ee S a e Ca ie s i O e a io o uck A i a a e o 1 5 ucks/ ou 113
ii LIST OF FIGURES
Figure Page
1 A ua co ai e a ic o U S o s 199 - 5 ( EU
o o Y/ o a co ai e s a EU s 1991- 7 5
3 1 e mi a i e aces 1
3 Ga e e a ce a e co ai e e mi a 11
3 3 uck s o a ea ei g se ice y s a e ca ie s 1
3 ock c a e o e a io a e e 13
1 Mi imi i g e com i a io o em y s a e is a ce a uck se ice ime 1
Mi imi i g e s a e ca ie ee si e 19
3 E am e o s a e ca ie o e a io i ime
5 1 Sc ema ic esc i io o e o e o e e i io o ou i es 3
1 Assig me ogic ase o C S u e 3
a i g e io a assig me ocess 5
3 ogic o u ga ia assig me wi ou e imi o uck wai ime ogic
Mo i ie assig me ocess 7
5 ogic o u ga ia a go i m wi io i y assig me 9
Assig me ocess 51
7 ogic o e Im ici E ume a io eu is ic 53
7 1 e mi a ayou 55
7 uck a i a is i u io e ou o o e a io 5
7 3 o a is a ce a e e as a u c io o s a e ca ie ee si e 59
7 A e age se ice ime e uck
7 5 A e age wai i g ime e uck LIST OF FIGURES (Continued) Figure Page
7 o a is a ce a e e y s a e ca ie s
7 7 A e age uck wai i g ime o se ice 3
7 ime e a se u i a ucks a e ocesse
7 9 o a is a ce a e e y s a e ca ie s
7 1 A e age uck wai i g ime o se ice 5
7 11 ime e a se u i a o s a e ocesse
7 1 o a is a ce a e e as a u c io o e ee si e a e a i g e io 7
7 13 A e age uck wai i g ime o se ice
7 1 ime e a se u i a ucks a e ocesse 9
1 O ima a i g e io a s a e ca ie ee si e o i e e uck a i a a es 77
o a ai y cos as a u c io o e ce age o im o ucks a i i g a e e mi a 7
3 A e age Cos e Co ai e
Sam e mea s a o e s a a e ia io a ge 1
9 1 o a ai y cos o o e a io o i e e s a e ca ie ee si e a uck a i a a e o 5 ucks/ ou 9 o a ai y cos o o e a io o i e e s a e ca ie ee si e a uck a i a a e o 1 5 ucks/ ou 5 9 3 is i u io o uck a i a s i a o e mi a y ou o e ay
9 ime i e a s ase o simi a uck a i a a es 7
1 o a cos o o e a io e ay o a i a a e o 5 ucks e ou 11
A e age uck wai i g ime o se ice o a i a a e o 5 ucks e ou 115
3 ime e a se u i a ucks a e ocesse o a i a a e o 5 ucks e ou 11
o a cos o o e a io e ay o a i a a e o 5 ucks e ou 117
5 C a ge i cos o e o e a io wi ee si e o si s a e ca ie s 11
i LIST OF FIGURES (Continued) Figure Page
A e age uck wai i g ime o se ice o a i a a e o 5 ucks e ou 11
7 ime e a se u i a ucks a e ocesse o a i a a e o 5 ucks e ou 119
o a cos o o e a io e ay o a i a a e o 75 ucks e ou 1
9 A e age uck wai i g ime o se ice o a i a a e o 75 ucks e ou 1
1 ime e a se u i a ucks a e ocesse o a i a a e o 75 ucks e ou 1 1
11 o a cos o o e a io e ay o a i a a e o 1 ucks e ou 1 1
1 A e age uck wai i g ime o se ice o a i a a e o 1 ucks e ou 1
13 ime e a se u i a ucks a e ocesse o a i a a e o 1 ucks e ou 1
1 o a cos o o e a io e ay o a i a a e o 1 5 ucks e ou 1 3
15 A e age uck wai i g ime o se ice o a i a a e o 1 5 ucks e ou 1
1 ime e a se u i a ucks a e ocesse o a i a a e o 1 5 ucks e ou 1
xv C A E
I O UC IO
. h rp f th rt t n
e u ose o is ocume is o ese me o s o im o i g e o uc i i y a e se ice qua i y o s a e ca ie o e a io s a a o e mi a e s a e ca ie is a mac i e a s a es a uck- ac o a emo es a oa e e o co ai e om e uck- ac o s c assis a
e i e s i o e ya o su seque oa i g o e s i o a ou ou oyage y sea I e o osi e i ec io i ecei es a oa e im o co ai e a e i e s i o e ucks o ai o e ou ou mo eme y a e o e a io is y amic si ce e s a es a ee o e assig e o co ai e s i ea ime a ei wo k oa is e e mi e y e a i a a es a ows o co ai e s
o is easo i is im e a i e o e e mi e e o ima co ai e a i g oce u e usi g e ea ime i o ma io ega i g equi me osi io a uck a i a s
e isse a io ese s a mo e o o imi i g e s a e ca ie ee si e a a i g
e io e esu s a sugges io s o im o i g o e a io s a e ese e e mo e e e o e wi a ow e mi a o e a o o o e a e e icie y a o e uce e cos o o e a io e im o a ce o is o em ies i a ee o im o e ai y o e a io s a e e mi a a o mee se ice eques s om i s cus ome s
.2 Or n z t n f th rt t n
e o ga i a io o is isse a io is as o ows C a e o i es e ea e wi
ackg ou i o ma io o e g o a e s i co ai e a ic A u e s a i g o e causes a mag i u e o is e is ee e o u e esea c i o a a ge e s ec i e
2
e co ai e e mi a s uc u e asic e eme s a o e a io is ese e i C a e 3 e o em s eci ic o e a si e o e a io o e co ai e e mi a a e o e o s a e ca ie s i e o sys em is ese e i C a e e o mu a io o e o em a is isse a io a swe s a
esea c ques io s a e ese e i C a e C a e 5 ese s e i e a u e e iew a a esses
e e ious wo k o e i e e o i g e icie equi me o e a io s i e mi a s A so e esea c i e e o i g simu a io mo e s o e o o e a io is ese e a a a y e C a e ese s
e me o o ogy a eu is ics use o so e e o em e esu s a e a a ysis o e esu s a e iscusse i C a e 7 e a a ysis o ow e s a e ca ie ee si e a e u a io o e
a i g e io a e im ac e y i e e uck a i a a es is ese e i C a e C a e 9
iscusse a e-o s e wee e cos o o e a io a se ice qua i y e o e a io wi a ia e s a e ca ie ee si e a a i g e io is ese e a e esu s a e com a e o e o e a io wi i e s a e ca ie ee si e e ici g s uc u e o a a oi me sys em is
iscusse a i s im ica io o a es a o e a o s ou c a ge is ese e C a e 1 ese s
e co c usio a sugges io s o u u e esea c C A E
ACKG OU
A co ai e o o i es a i e ace e wee s i s ai oa s a ucks a e ese s a c i ica
i k i e i e mo a c ai I e as ew yea s i c ease i e a io a a e esu e i u ece e e g ow i co ai e i e eig o umes a e y o e mi a s wo wi e e i c ease i co ai e o ume u s essu e o o ma ageme o accommo a e ew eques s om c ie s o as ime y a e icie se ice
A so isi g com e i io amo g o s a e i o uc io o ig ca aci y s i s i o e a io
as u e o mous essu es o o ma ageme o e e o e icie co ai e a i g ocess o
u e i c ease ca aci y o e e mi a a s ee u e a ss i me ocesses a c a ge i co ai e a i g o e a io a ac ices was ecessa y a is e o e mo i ica io o e mi a ayou s as we as sys ems esig
e g ow o co ai e i e ca go i c ease e ema o e a a ea a e mi a s o s o i g co ai e s O e e e is co ges io a e a access si e o e o e mi a a o e
ig way e wo k i e o e mi a ici i y ue o a ge uck queues some a ge co ai e e mi a s a e o ay ucke s o ei e cessi e wai i g ime
Si ce e ma ke equi es o s o i c ease ei oug u a ecause ma y o s a e wo ki g a o c ose o ca aci y ew me o s a oo s a e ee e a e a e e mi a o e a o s o
e e sy c o i e ac i i ies wi i e e mi a Ei e ew e icie equi me ee s o e e oye o ew o e a io a me o s ee o e u i ac ice o a com i a io o o o assu e e icie
e mi a o e a io is is im o a ecause e com e i io e wee e mi a o e a o s a e e se ice ca a ac s i e s o ake ei usi ess o a i e e e mi a
Intermodal - being or involving transportation by more than one form of carrier during a single journey
3
1 I c easi g e s i G o a Co ai e a ic
e o a co ai e a ic o ume o e o co ai e o s wi o ume o mo e a o e mi io
EUs2 eac e 97 mi io EU i 5 a 1 9 e ce i c ease com a e o e a a ysis
a as ee o e y e IS (I s i u e o S i i g Eco omics a ogis ics 5 i c u e 77 o s
(3 Asia /Ocea ia o s 19 Eu o ea o s 1 Ame ica o s a o s oca e i A ica I
5 a o ima e y 5 o e wo co ai e a ic i e ms o EU was a i u e o Asia
o s w e e y e o eig C i ese o s a o e e ese e 5 o e o a co ai e a ic
Eu o e s s a e was 1 5 o e wo co ai e o a ic a Ame ica s 15 e o C i ese mai a co ai e o s (wi ou o g Ko g g ew o a e age y mo e a 5 e yea ei a ua co ai e a ic was 13 mi io EU i 1999 a 5 5 mi EU i 5 es ec i e y
e co ai e i e a ic i e U S o s is i c easi g a i y e e y yea es ecia y e
a ic o e US Wes Coas o s e 1 U S o s a e 5 e ce o e U S co ai e i e
a ic i 5 ( u eau o a s o a io S a is ics igu e 1 s ows e i c easi g e i g ow o co ai e a ic i e US o s e wee 199 a 5 e as 1 yea e io o w ic o ume
a a a e a ai a e
igu e 1 A ua co ai e a ic o U S o s 199 - 5 ( EU
2 TEU — twenty foot equivalent unit, a measure used in intermodal transport 5
North American Pacific (West Coast) ports have strong relationships with the Asian ports.
Their traffic is more than 90 percent distributed to and from the Far East. This interrelation is underlined by the analysis of monthly container traffic of North American West Coast ports done by the ISL and demonstrates an increasing trend in container traffic going through major U.S. Ports.
For the North American East Coast ports the Asian market is of significant importance. The ports of New York/New Jersey's top trading partners are located in Asia. About 50 per cent of the ports container traffic in 2005 was related to the trade with Asia and every year the container volume
that goes through the ports of New York/New Jersey is increasing. The trend is shown in Figure 2.2.
Figure 2.2 Port of NY/NJ total containers and TEU's 1991-2007
Table 2.1 show that the containerized cargo volumes in the Port of New York and New Jersey
rose nearly 7% from 2005 to 2006. This is followed by a continued growth in trade with the Far East,
North Europe and Southeast Asia. ExpressRail, the Port Authority's on-dock rail terminals in New
Jersey, set a new record in 2006, handling 338,882 containers, 11.8 percent more than in 2005 (Port
Authority of New York and New Jersey), hi the next 10 years, nearly $2 billion in infrastructure
upgrades are planned for the Port Authority's marine terminal facilities and for off-port roads and
railways to improve the flow of cargo. 6
bl 2. e s i Co ai e o ume a e o o ew Yo k/ ew e sey
Co ai e a e 5 C a ge O e 5 oa e Im o EUs 1 1 599 55 7 9 oa e E o EUs 97 1 51 37 7 o a oa e EUs 3 3 5 3 3 5 9 7 9 o a EUs ( oa s a 7 5 31 5 9 em ies o a Co ai e s ( oa s a 7 991 em ies
Sou ce ou a o Comme ce — IE S o Au o i y o ew Yo k & ew e sey aci i y Cou s
e i-S a e a s o a io Cam aig e ic s a eig o age mo i g oug ew
e sey a s o e y uck wi i c ease o 77 y e A Y s a is ics s ow a o
e ca go a s o e o a om e o e mi a s is y uck ( o Au o i y o ew Yo k a ew
e sey
e a a i ica e a e i c easi g co ai e a ic u s essu e o co ai e e mi a ma ageme o e iew ei o e a io a ac ices a make a y c a ges ee e so a ey ca co i ue o o i e a equa e se ice o ei cus ome s
2.2 E n f rt Op r t n
o e mi a o e a o s a e o co si e e o owi g cos s
• a ease Cos A o e mi a o e a o is usua y easi g e a o a o e mi a om
a u ic e i y o e am e e mi a o e a o s a e o o ew Yo k/ ew e sey a e
easi g e a om e o Au o i y o ew Yo k a ew e sey a i-s a e u ic
age cy e us e wi ma agi g o a ai o aci i ies i e ew Yo k me o o i a
egio
• Ca i a cos s e o e mi a is uyi g e mi a i as uc u e a ca go a i g
equi me suc as uck ga e equi me c a es s a e ca ie s o oa e s e c 7
• O e a i g e e ses i c u e e e a e cos s o a o u i i ies a equi me usage a mai e a ce
• O e ea e e ses suc as i su a ce ma ke i g e c
e a o e cos s e e mi e e u i cos e co ai e a e e mi a o e a o uses o
e e mi e e a es/ ees a c a ge o c ie s usua y s eams i i es e ca i a ease a a o
e o e ea cos ca e co si e e i e cos s (i e ey a e i e e e o e co ai e o ume
a e y e e mi a owe e ey ee o e a oca e o e co ai e s assi g oug e
e mi a A ig e oug u wou a s a e i o a owe a oca e u i cos ( e co ai e associa e wi ese cos ca ego ies O e o e a e ma imum e mi a oug u is
e e mi e y e e mi a ca aci y a is su ec o e c a ac e is ics o e mi a esig co ai e
a i g equi me wo k ow a o e a i g ac ices esig e o a e co ai e s as we as e
equi e e e o se ice a ame e s o i c ease e ca aci y a a ow ig e oug u e
e mi a o e a o as o ei e u c ase ew equi me c a ge/im o e wo k ow o e a e e e o se ice equi eme s u c asi g o easi g ew equi me wi esu i ig e u i cos
e uci g e e e o se ice may esu i cus ome issa is ac io e e ua y causi g some o em
o s o usi g e e mi a e e o e e mos cos -e ec i e measu e a e mi a ca ake o i c ease ca aci y o a gi e equi me oo a e e o se ice equi eme s is o ee a ua e e e mi a o e a io s a wo k ocesses a seek im o eme s y e e o i g a ew s a egy o me o o ogy o a i g co ai e s C a e wi emo s a e a is is ue
3 o I es me s a e s ows e cos o a e ca i a im o eme s a wi e im eme e i o s o ew Yo k & ew e sey o accommo a e a ici a e g ow i co ai e ema e a e s ows a a o ima e y o e i o e i es me s wi e use o accommo a e a ge s i s a 8 mai ai e c a e ; e emi e o e u ge wou e use o o i as uc u e im o eme s a e mi a e e o me
Table 2.2 a e Ca i a i es me s i e o o ew Yo k a ew e sey om 7- 1 og am $ Mi io s C a e ee e i g 79 Ma i e e mi a e e o me a e e e o me 1 o oa way Im o eme s a Sa e y 3 7 ai Ca go I as uc u e 3 S a e o Goo e ai 195 Secu i y o a 1 9 3 Sou ce o Au o i y o ew Yo k a ew e sey a e S a is ics
Si ce e o e a io s o a co ai e o a e i ue ce y a a ge um e o i e ac i g
ac o s ( e so e a yi g s i a uck a i a a e s a ious ki s o ca go- a i g equi me o imi a io o suc a com e sys em is a c a e gi g ask Commu ica io wi e e a a ies suc as s i i g i es age s uck a ai com a ies w ic o i e o ma ageme wi i o ma io ecessa y o e e a ei o e a io ac i i ies is c ucia i
e e o i g a o e o e a io s a o e o CHAPTER 3
DESCRIPTION OF A PORT CONTAINER TERMINAL OPERATION
I C a e e g ow e s i co ai e a ic we e esc i e a e cos s a a o e mi a
aces w e es a is i g a o e a io we e i e i ie is c a e ese s e o co ai e e mi a s uc u e use i is isse a io A oug e equi me a ayou a y om o o o w a is
ese e e e is a e y ica e co ai e a i g ocesses a occu i e e mi a a e i e i ie a esc i e a e o e o s a e ca ie as a co ai e a i g equi me is e ai e
3.1 Terminal Description
A o se es as a i e ace e wee ocea a a a s o mo es a a em o a y s o age aci i y
o co ai e s mo e y ese mo es I ge e a e ms a co ai e e mi a ca e esc i e as a sys em a co g ega es wo i e aces e a si e a ocksi e i e ace e a si e i e ace s ow i igu e 3 1 o i es se ice o ucks a ai s w e e e ica e a i g equi me
a s o co ai e s e wee e uck a ai a ea a e co ai e ya e ocksi e a ea
o i es se ice o s i s (u oa i g a oa i g o co ai e s o a om e esse a a ow o co ai e s e wee e co ai e ya a ocks e ya a ea is se a a e i o i e e s acks (o ocks e s acks a e se a a e i o sec io s o im o e o s ecia a em y co ai e s
e e may e a sec io o a i g s ecia co ai e s S ecia co ai e s a e ose a
a s o a a ous ma e ia s o a e e ige a e (ca e o e ee e s e ee e s equi e e ec ic se ice w i e ey a e i e ya a e e is a ee o a s ecia sec io o ese co ai e s
9 1
igu e 3 1 e mi a i e aces
A a y ica ma i e co ai e e mi a e e a e ou asic ogis ics u c io s a a e
e o me ecei i g s o age s agi g a oa i g ecei i g i o es aki g cus o y o e o o ou ou co ai e s om cus ome s (ca e s i e s o e su seque oa i g o ese i o esse s
e ou ou co ai e s a e oug i y uck o o - ock ai I ou co ai e s (o im o s a e u oa e om esse s o e icke u y ecei e s (ca e co sig ees e i ou co ai e s a e
icke u y ucks o e i e e o e o - ock ai aci i y o a u e i e au mo eme y ai
S o age is e u c io o aci g a co ai e o e e mi a i a k ow a eco e oca io so a i ca e easi y e ie e S agi g is a u c io o e a i g a co ai e o ea e e e mi a e
oa i g u c io i o es aci g a co ai e o a s i a uck o o - ock ai 11
3 a si e A ea u c io s
3 1 O e a io s a e Ga e
e ocesses occu i g a e a si e a ea a e com ise o i e e e e o e a io s a gi g
om co ai e a i a s y uck (o ai a a ious e a e co ai e a i g o e a io s
e o me y ya equi me Co ai e s a i i g y ucks a e e e i g u e ga e s ow i
igu e 3 a ei c eck-i is usua y a mu is age ocess
igu e 3 Ga e e a ce a e co ai e e mi a
e i s s e is e sc ee i g a a o a o uck i e s o e e e ga e A e ga e e
uck i e i o ma io com a y a u ose a e e i ie y e ga e o e a o e ga e o e a o
eco s e co ai e i o ma io i s ec s e uck a co ai e y came a a a e e i e i ica io ocess is com e e e uck is i ec e o ocee o a s o oca io w e e i wi e
ocesse y ya equi me e co ai e i o ma io associa e wi e uck a i a is e ie e
om e a a ase a asse o o e sc e u i g ou i e a assig s e ya equi me o se ice 1
is a sac io a ici a s i e o o e a io u i i e e E I (e ec o ic a a i e c a ge w ic a ows e e ec o ic e c a ge o i o ma io amo g e mi a c ie s ucke s a o e a ies
3 uck Se ice y Ya Equi me
e a i i g uck as ee assig e a esig a e s o i e a ki g a ea o e ya a a a icu a
iece o ya equi me ca e s a e ca ie is assig e o i o e ucks e i e i g a e o
(ou ou co ai e e s a e ca ie emo es e co ai e om a uck- ac o c assis I oes
a y s a i g e c assis a i i g e co ai e usi g a o e ea c a e e s a e e
i es away ca yi g e co ai e i i s e y I e uck is icki g u a im o co ai e e s a e ca ie oca es e co ai e i e ya a a s o s i o e uck s o i g a ea a oa s i o e uck s c assis A s a s o o e o e a io is ese e i igu e 3 3 I is igu e e s a e a e e o e uck s o i g a ea a e us emo e a co ai e om e uck w i e e s a e i e owe ig as us e i e e e co ai e o e uck
igu e 3 3 uck s o a ea ei g se ice y s a e ca ie s 13
e co ai e oca io i e ya is gi e y ock ow a ie wi i e ock a is assig e o a s a e ca ie i ea ime u o a i a o e co ai e a e e mi a I some co ai e e mi a s ucks a e i ec e o a k e o e s ack w e e e co ai e ey a e ca yi g
as o e u oa e om ( oa e o Usua y u e i e ga y c a es ( GC a e e gage o is
y e o o e a io
3 3 ocksi e A ea u c io s
W e a s i a i es e u oa i g is e o me y c a es as s ow i igu e 3 e c a es u oa co ai e s i e a ea e o em w e e e co ai e s a e e icke u y s a e ca ie s o
G c a es a e e i e e o e s o age ya I some e mi a s co ai e s a e oa e i ec y o
Au oma e Gui e e ic es (AG s o e e mi a s i e a ucks a a e use as a
a s o a io i k e wee e ya a e ock c a es Co ai e s a e e u oa e a
a s e e o ei esig a e osi io i e s ack y s a e ca ie s o ya c a es Cus oms i s ec io o im o co ai e s is equi e a i may occu o ock o some o e esig a e
oca io
igu e 3 ock c a e o e a io a e e 4
e im o a e o s acks o co ai e s a e usua y se a a e i e ya so a i e e
asks e a e o ose co ai e s ca e e o me is se a a io may a e ia e ya o e a io co ges io e ou ou co ai e s a a e icke u om e ya ock a e a s o e o e
e a o e o a a s acki g osi io a is e- e i e i e s owage a so a ey ca e
icke y e ock c a es o e oa e o o e esse e s owage a o i es i o ma io o e
e ie a a mo eme o e e o co ai e s o e e o oa i g CHAPTER 4
STRADDLE CARRIER PROBLEM DESCRIPTION
C a e 3 ese e a esc i io o a y ica co ai e e mi a o e a io e co ai e a i g
ocesses we e i e i ie o eac o e e mi a sec io s is c a e a a y es e a si e o e a io a icu a y e se ice a as o e o i e o ucks a i i g a e o o e i e e o a ick u im o co ai e s e e ec a io s om ucke s a o e a o s i e ms o se ice qua i y a e esc i e e c a e co c u es wi a e i i io o e esea c o ec i e e ques io s o w ic is isse a io wi gi e a a swe a e i e i ie as we
4.1 Introduction
e a i a o uck- ac o s wi co ai e s is a a om ocess i a i io o e u ce ai ies
e a e o e a i a s ig - io i y o ime-se si i e uck eques s a ise u i g e o e a io a wi ou a a ce wa i g is com e i y makes e ee o a y amic ecisio -maki g e e mo e a a e a e o e a io a e e y ecog i i g ese u ce ai ies a u i i i g ea - ime i o ma io o uck se ice e ec a io s e o o e a o ca e e o a o ima s a e ca ie
is a c i g s a egy a ca ea owa ig e o i a i i y w i e e i e i g su e io se ice o ucks
A e o e a io a e e e im o a ecisio is a ou e o ima oca io w e e co ai e
ee s o e isc a ge om e s i / uck/ ai a ow e co ai e a i g equi me ( e c a es ya c a es s a e ca ie s AG s e c ee o e ou e i is com e e wo k o ma imi e o uc i i y e co o a y ques io is w a a e e e ec i e me o s o co o e equi me a e su e a a esi e esu is ac ie e
15 6
Co ai e e mi a o e a io s ee o e o imi e i ea - ime ecause mos o e
ocesses a occu ca o e o esee i a a ce e a a ega i g e i e y o co ai e s o e
e mi a y uck may e k ow om e E I owe e e e ac ime w e a co ai e wi a i e a e e mi a is u k ow
As ucks a e o a e o a si io oi s w e e co ai e s a e icke y s a e ca ie s o c a es e uck seque ces a e ga e a a e a si io oi oes o a e o e e same si ce e ocessi g ime o e uck a e ga e is i e e o eac uck
4.2 h Str ddl C rr r t r A n nt r bl
e e mi a o e a o s mos im o a co ce is o i c ease e equi me o uc i i y is
a s a es i o mi imi i g s a e i e a u o uc i e ime e e o e e o e a o wis es o
e oy e ecessa y s a e ca ie ee a wi a e a mi imum amou o u o uc i e (em y mo es w i e o i i g acce a e uck se ice i e ms o wai i g ime Si ce e osi io o e co ai e a uck i e ya is k ow e oa e a e is a ce e wee co ai e a uck ca
e easi y ca cu a e a i s i e o a i W a is o k ow is e u o uc i e em y a e
is a ce i su o o e oa e mo e y e uci g is em y a e e u i i a io o e s a e ca ie is i c ease a wea a ea is e uce
uck wai i g ime is e i e as e ime e io om e mome a uck e e s e s o a
e ime i egi s o e se ice y a s a e ca ie is ime is o co ce o e e y e mi a o e a o ecause i is ime is e uce e cus ome sa is ac io is i c ease us i is im e a i e
o e e mi a o e a o o im o e o uc i i y o i s o e a io (a mi imi e i s cos w i e ei g cog i a o e cus ome e ec a io s i e ms o uck wai ime as a measu e o se ice qua i y
e o ec i e o e e mi a o e a o o im o i g o uc i i y is accom is e y mi imi i g e em y a e o s a e ca ie s a a e same ime im o i g cus ome se ice y mi imi i g e ays Em y a e is e i e as a mo eme o s a e ca ie wi o co ai e o
oa e e ay i cus ome se ice is commo y measu e y e e g o ime a a i i g uck wai s i e s o e o e i is se ice I a i io o e e mi a sc e u e s o ec i es ma ageme wou a so ike o mi imi e e s a e ca ie ee si e i o e a io is wi e uce e ca i a cos o s a e ca ie s a e a ua i e a a ia e cos s a occu om e use o equi me
O e o e a s i e s wa o mi imi e e a si ime a ey wou ike a e
uck se ice ime i o is as e so a ey ca e uce i e o y cos e s i e s ecisio o w e e o co i ue o use e e mi a is ase o e se ice qua i y ecei e om e e mi a o e a o I e se ice ime is u sa is ac o y e s i e ca c a ge e e mi a e ucke s wou
ike o e se ice as e si ce ey a e ai y e oa a o y e ou
4. r h M t v t n
4. . t nt l f r d t n n Op r t n C t nd r S rv
e o owi g wo e am es a e e e o e y S aso ic a Si e is (199 o emo s a e a a i e ige assig me cou ec ease e o e a o s cos as we as im o e cus ome se ice e e am e o e o e a io s ow i igu e 1 co sis s o wo co ai e s a a e o e e i e e
om e ya o e ucks i e s o i g a ea e assum io s ma e a e a uck 1 ( 1 a i e a was s o e a a m w i e uck ( a i e o e mi u e a e uck 1 is icki g u co ai e 1 (C1 w i e uck ee s o ick u co ai e (C e e a e wo s a e ca ie s a a e i o e a io a S a e 1 (S1 is a ai a e o e ew assig me w i e s a e (S wi e a ai a e o a ew assig me i o e mi u e I e s a es a e assig e usi g i s -come- i s - se e (o e c oses co ai e u e s a e 1 wi e assig e o ocess co ai e 1 w i e s a e
wi e assig e o ocess co ai e e a e age s ee o e s a e ca ie is assume o e
1 m e o a is a ce a ime a a s a e ca ie ee s o a e se o eac a co ai e is mi es a 1 sec es ec i e y ( a e o igu e 1 I e assig me ecisio was os o e o 1 mi u e a e o s a es assig e a e same ime wi e o ec i e o mi imi i g e o a 8 distance travelled, a better solution would have been obtained. The solution assigns straddle 1 to move container 2 and straddle 2 to move container 1, The result of the assignment, shown in the table of Figure 4,1, demonstrated that empty travel distance decreased by 33% (from 0.6 miles to 0.4 miles) and truck service time decreased by 6% (from 216 to 204 seconds). This shows that straddle carriers can be used more efficiently and at the same time improvements in productivity and service quality can be achieved.
r 4. Minimizing the combination of empty straddle distance and truck service time
4. .2 h r d Off t n Op r t r C t nd r S rv
The example in Figure 4,2 demonstrated how intelligent assignment may further reduce the operator's cost while the customer service time may remain the same or slightly worse. The container C1 needs to be unloaded from the truck and delivered to the yard location L 1 . Three minutes later a truck (T2) is slotted and container C2 has to be loaded to a truck chassis from the yard. If both straddle carriers are used in processing these two service requests then the solution is in the left column in table on
Figure 4,2, If we were to use only one straddle carrier to process both trucks, then the assignment so u io is S1-C1 a S1-C y e imi a i g o e s a e om e o e a io e ma gi a i c ease i
e uck se ice ime o 3 sec occu e ec easi g e um e o s a e ca ie s i o e a io
esu s i a su s a ia e uc io i e o e a o s ow e s i a mai e a ce cos
r 4.2 Mi imi i g e s a e ca ie ee si e
Mo ok e a (1995 co uc e a s u y o im o i g e ig way o e a io (ca e ayage o e ai - uck i e mo a a s o a io se ice e ai - uck i e mo a se ice co sis s o uck o e a io s a a s o a oa om e e mi a o co sig ees a om s i e s o e e mi a e
ai o e a io is use o a s o ai e s wi co ai e s e wee i e mo a e mi a s e s u y
ese s e me o o ogy o eo ga i i g ayage o e a io wi e goa o im o i g se ice qua i y a e uci g e cos o se ice a e same ime
4. . r Op r t n
A uck wi a em y ai e o co ai e is is a c e om e i e mo a e mi a o a s i e s
oca io o ick u a oa wo sce a ios a e ossi e i is case e i s is a a uck is goi g o wai wi a ai e u i i is oa e a e e u e ai e o e e mi a o e ai mo eme I
e seco sce a io e uck wi ea e e ai e o oa i g a e u o e e mi a wi ou e
ai e O e y es o o e a io s co uc e a e e e i e y o ca go o co sig ees a e osi io i g o em y ai e s
O e o e easo s w y i e mo a se ice oes o a e ig e s a e o e o g au se ice is ecause e ayage se ice is a oca e amo g ma y i e e e ucke s a eac o
em is co o i g a sc e u i g ei o e a io i e e e y o o e a o e is esu s i ma y u ecessa y o - e e ue mo eme s us e i ea was o o se e a ayage o e a io as a sys em a a i g i o mee e ema a se ice qua i y a a mi imum cos
3 3 1 Se ice Im o eme a Mo e i g
Se ice qua i y ca e im o e y sc e u i g uck mo es i a a ce e e i e y o co sig ees is e o me wi i ew ou s o e ca go ei g emo e om e ai a ca go ick- u eques s a e gi e o ayage com a ies a eas o e o mo e ays i a a ce e i ea is o ai mo es a e uce e em y mi eage o ucks I c easi g e oa e si y cou esu i ec easi g
o - e e ue uck mi es a us cos y ai i g mo eme s ewe ucks a e ee e o ocess se ice eques s a ec ease o e cos e oa is ossi e
A ma ema ica mo e o ayage was e e o e a i was use o e a ua e e cos o o e a io w e uck mo eme s a e ce a y a e e mo e s o ec i e was o mi imi e o a ayage a o e a i g cos y se ec i g ai e mo eme imes a oca io s a assig i g ucks o
ose mo eme s e co s ai s a e a a i ou ai e s om e e mi a o co sig ees a e o
e e i e e wi i s eci ic ime co s ai em y ai e s o s i e as o e e i e e o oa i g a e icke -u wi i a s eci ic ime e io a e osi io i g o ai e s o a oi ei accumu a io as o e e o me
e esu s s owe a im o eme s i o e a io a e ossi e a cos e uc io ca e ac ie e a e same ime A so a im o a i i g was a e ig e oa e si y is e e a e mo e o o u i ies o ask ai i g a us usi g ewe ucks o e icie o e a io e eake e
is a ce w e e i e mo a se ice is com e i i e o uck se ice is a so e uce e eo ga i a io 2 a e e use o i o ma io c ea y o i e o a e cie sc e u i g a ici g o ayage se ice
4. . .2 C n l n
e ques io is ow e me o o ogy e e o e o ayage o e a io ese e a o e ca e a ie i is case I case o i e mo a se ice e a a was a ai a e o e w o e ay a o y a sma o io o ayage eques s o se ice was u k ow e i o ma io a ou u u e e e s e a e e e a i g a e icie ayage o e a io I e case o uck se ice i e o e a i a a e is muc ig e a i wou e ossi e o e e mi a o e a o o wai o a o g ime o ga e e i o ma io e a e wi eac uck se ice eques
4.4 Und r t nd n th t n f Opt l t n l S l t n
Si ce e uck a i a ime a a co ai e e mi a is a u k ow a ia e ucks e o e
ocesse i e o e o a i a e e a e ee a em s o i o uce a oi me s (a ese e ime
o ucks ca i g a e e mi a so a e e mi a ca e e e e o i s co ai e a i g o e a io s a egy u so a e uck a i a is s i a om A ea ime ocess o s a e ca ie o e a io is i us a e i igu e 3 I is o e a io s a e ca ie SC1 is is a c e o i g a co ai e o uck 1 as soo as uck 1 e e s e uck s o a ea SC1 e i e s e co ai e o
uck 1 a w i e uck 1 is ea i g e s o SC1 is e osi io e o se e uck w ic a
oug a e o co ai e o e e mi a SC1 emo es (o s i s i e o e mi a a a ce e co ai e om uck a e i e s i o a a ki g s o i e ya SC1 e e u s em y o e uck s o a ea a emo es e oa e co ai e om uck 22
r 4. E am e o s a e ca ie o e a io i ime
y o se i g e o e a io o e ca a i e o e o owi g co c usio s a ou e o ima i y o e assig me o s a e ca ie s o ucks
• Si ce e a i a imes o ucks u i g e ay a e oca io s o co ai e s (associa e
wi e ucks i e ya a e k ow o e ca e e o a o ima so u io o e s a e
ca ie assig me o em is so u io wi a e o ima o seque ce o s a es i
e ms o e wo k eac s a e ee s o e o m W i e is so u io wou e o ima i
e ms o mi imi i g s a e em y mi eage a uck wai ime i is im ossi e o
im eme i i e ea wo ecause i wou i o e " ee i g e o e a io i ime" u i
a o ima "s a ic" assig me ca e ma e is o ima s a ic o e a io wou gi e a
eo e ica owe ou o cos s
• e e ac o osi e o e a o e wou e o make e o ima assig me eac ime a
s a e ecomes a ai a e U e is assig me e s a e wou e assig e o e 2
c oses uck o e o e a as wai e e o ges o a weig e com i a io o ose wo
o ec i es W i e is assig me may e e es i a a icu a mome e o e a i g
s a egy a co sis s o a se o ese assig me s wi o ea o e o e a o ima so u io o e e i e o e a i g ay e imme iacy o ese assig me s (w i e o ima a a
mome wi o co si e e o ima assig me o e ia a a ises om os o i g e
sc e u e u i a e e assig me ca e ma e as iscusse a o e i Sec io s 3 1 a 3
• e e o e i is u e o i o uce a co ce o a a i g e io — a ime i e a wi i
w ic o e ca i e o ima so u io o e s a e assig me o em is co ce
wou mea a o e wou a ow uck a i a s a se ice eques s o accumu a e u i g a
ime i e a o ce ai u a io a a e e o a e io a o ima s a e assig me
wi e e e o e o e am e gi e e e s a oca io s u o ime e o ima sc e u e
wou e im eme e a ime (o +1 e e issue wou e o a y e e g o is
a i g e io o i e o e a yie s e es o ima so u io o e e i e ay s
o e a io y a yi g e e g o e a i g e io o e ca e o e e o e ia a
ma c i g o o u i ies may yie i im o i g e o e a o ima so u io o e ay s
o e a io O ce e "o ima u a io o e a i g o i o " as ee e e mi e e is
a i g o i o ca e im eme e i a si ua io s wi simi a a i a a e s
Und rl n r n pl h nd th l nn n r d
I is wo i es iga i g e g o e ime wi ow (ca e a i g e io a wi gi e e oug i o ma io a ou uck se ice eques s o e a e e mi a o e a o o e icie y a o e a io s
o e am e i s ea o ocessi g o e se ice eques a a ime may e e k ow e ge a ou e e
e e e s wi gi e e o o u i y o e e em oy e a i g equi me Si ce e e si y o
uck a i a s is ig a e um e o se ice eques i s o e io o ime is ig may e i is
ossi e o e ay uck se ice y some s o e io o ime o gi e e e mi a o e a o i o ma io a ou e uck oca io i e s o s a e e ac oca io o e co ai e i e ya a is associa e wi a uck y a yi g e a i g e io we ca ake a a age o o o u i ies o e icie ma c a e e ge o e a i g e io e e o e e ques io a a ises is w e e e o ima e g o e a i g e io is 1 5 o 1 mi u es o mo e
5 O ec i es e o ec i e o is isse a io is o e e o a ew a oac o o imi i g co ai e a i g equi me o e a io s a wi e e e icia o e e mi a o e a o i e ms o im o i g
o uc i i y o i i g as e se ice a e uci g o e a i g cos e o ose so u io ee s o a so o i e acce a e se ice o ucke s e o e a io a s a egies a a e ese e wi e a e o assess e o e a io om ucke s a e mi a o e a o s e s ec i e
e mai ques io a e e mi a o e a o aces is "Ca I o i e a equa e qua i y o se ice a a e same ime mi imi e my ca i a a o e a i g cos e a e o e s a e ca ie o e a io ? e ques io s a is isse a io a swe s a e s a e as o ows
1 W a is e o ima assig me o s a e ca ie s o ucks gi e e ime e e e a u e o e assig me ?
W a is e o ima e g o e a i g e io ?
3 W a is e o ima s a e ca ie ee si e a ee s o e e oye o se ice e ucks?
W a is e e a io s i e wee e uck a i a a e gi e a s a e ca ie ee si e
equi eme a e a i g e io e g ? ow e a i a a e im ac s e s a e ca ie ee si e a e a i g e io ?
5 W a is e e a io s i e wee e a i a a e e cos o o e a io e s a e ca ie ee si e a e a i g e io ?
ow a e cos s c a gi g wi e c a ge i s a e ca ie ee si e a a i g o i o ?
A e e e eco omies o sca e i e o e a io ? CHAPTER 5
LITERATURE REVIEW
Mos s u ies o e o a i g a simu a io ocus o e wa e si e a e a o e a si e —
ame y ey a e co ce e wi im o i g s i se ice a e a uck se ice e easo o is
ias is a a s i s ow ime cos s a cus ome ema s a e ig e a mo e c i ica a ei a cou e a s is oes o mea a o imi i g uck se ici g a equi me u i i a io is o o im o a ce Si ce a e mi a s e o ma ce is u ge o e o e a e o ma ce o i s i i i ua com o e s is ias is o us i e
e i e a u e e iew co sis s o ee segme s
- A go i ms a o e a io s esea c ec iques use o i e e o ems o assig i g
equi me o s i s o ucks as a se a a e sys em
- O e a io o a co ai e e mi a as a sys em e mi a esig e a ua io a e mi a simu a io as a com e e sys em
- e iew o e a go i m use o so e e o ec i e o is isse a io
e sec io s a o ows a e o ga i e ase o e a o e segme s
5.1 Algorithms for Assigning Equipment at a Container Terminal
e S a e Sc e u i g oce u e (SS co si e s e e e e cy e wee seque ia assig me
oce u es o so e e o em o assig i g s a e ca ie s w e ey a i e o e s o e a ea
( as a S aso ic 1999 is me o o ogy a em s o ai c ose y oca e o -o o s wi
icku o s I e s a e ca ie is a ai a e i e s o a ea e e i s e e e ce is o assig i o a o -o uck a is a ea y s o e
25 26
The assignment algorithm is used as a base for this procedure, and the whole routing consists of six steps, When a new truck is slotted, the SSP basically checks which straddle carrier is available and assigns the nearest one. If the straddle carrier is available, the SSP compute the assignment cost for each truck, which is weighted objective function, and by using Hungarian method finds the best assignment of straddle carriers to trucks. The Straddle Scheduling Procedure (SSP), based on the experiments, was being compared to the Closest Job Assignment method and Greedy Assignment
Procedure and it provided significant savings in empty straddle travel when compared to the other two methods. Also, by reducing the straddle carrier fleet size the waiting time increased by a small margin and it was shown that the value of SSP scheduling increases as material handling resources are more constrained, Also, a signifrcant reduction in net schedule cost was made and truck waiting time was smaller compared to the alternative strategies.
Different methods for scheduling straddle carriers, automated guided vehicles (AGV's), reefer workers and stacking cranes were tested in a container terminal system (Hartman 2004), The straddle carrier operation is demonstrated on three cases where straddle carriers provide service to quay cranes or external trucks by delivering containers from the yard. The assignment of straddle carriers has the objective of minimizing the waiting time of quay cranes and trucks on containers. The first heuristic that was used to dispatch equipment to jobs is a Priority rule based heuristic that follows four steps (compute eligible jobs, select job, select resource and update schedule) to schedule all jobs. This heuristic was used on two methods; the first method is a single pass dispatching method that produces one schedule. The Second method is a multi pass sampling method that produces different schedules by randomizing the minimum due date priority rule,
The application of the Genetic Algorithm, which adopts the principles of biological evolution to solve optimization problems, was also used to solve the assignment problem, The genetic
Algorithm often does not operate on schedules but on representation of schedules, The results showed that the genetic algorithm produces better results than the priority rule methods. The initial population 27 and the sampling process, had an impact on the results obtained by implementing Genetic Algorithm scheduling method.
The real time optimization assignment of AGV's to service quay cranes and trucks was the focus of research done by Briskon et al. (2004). To assign AGV's, a Greedy Priority rule heuristic optimized the time at which an AGV should arrive at a quay crane. The main goal is to maximize productivity of quay cranes (i,e., if more containers are handled by quay cranes then there will be a shorter turnaround time of ships). To achieve higher productivity, the waiting time of quay cranes on
AGV's has to be minimized, To reach that goal, for example, the minimization of empty travel time of AGV's or a better distribution of AGV's to quay cranes can be an objective. The heuristic tries to prevent the AGV of arriving early or being late at the quay crane, which means that an AGV has to reach the quay crane just in time when it becomes available. If an AGV arrives early, it will wait for the quay crane to become available and that is a waste of AGVs time. If the AGV comes late, the quay crane is waiting for an AGV. The assignment is solved by using the Hungarian algorithm with the objective function of minimizing the due time that is calculated as a time between the arrival time of AGV to the quay crane and the time when the quay crane is ready to service that particular AGV,
The second solution is obtained by using the Greedy Heuristic with a Priority Rule. The first step is to select a job with the smallest due time, This is accomplished by selecting the AGV that leads to the smallest possible increase in the objective function. An Inventory-based approach treats a quay crane as a customer. Each quay crane has a buffer which contains AGV's that are assigned to that quay crane. Every time when AGV gets a job it is assigned to the quay crane that has the smallest buffer.
To compare the results of the Hungarian and the Greedy Heuristic assignment with the Inventory rule, assignment simulation is used, The Hungarian algorithm compared to the Greedy heuristic only increased productivity by 1.0-1.8%. The smallest empty travel times of AGV's are obtained by using the inventory based approach,
The assignment of straddle carriers at a container terminal was evaluated by Steenken (1993) with the goal to minimize distance the straddles traverse without carrying a load. Different heuristics 28 we e a ie o so e is o em e a a ce a Co ec a oac s a s wi so i g e assig me o em e i i ia s e is o a a ce e e wo k a e co ec e e wo k y so i g a mi imum s a i g ee o em e a a ce a Co ec mo e is e a e o e
Mu i e u a os ma o em (M A o e mo e use o ma c s a e ca ie s o o s is
ase o a Mac i e sc e u i g (MAS a co ai s ee is a c i g u es
- S o es ocessi g ime (S e o s wi s o es ocessi g ime a e a io i y o e assig e i s
- o ges ocessi g ime ( o s a e assig e ase o ec easi g ocessi g ime
- Ea ies ue a e (E o s a e a ke ase o i c easi g ue a e
e MAS mo e was com a e o e M a e esu s s owe a e MAS ou e o me
e M W e e o se e o e a io a ime e io was e e e o o e week o a a e M
a e e esu s y e uci g e em y oa i s y com a e o ac ie e y MAS
A a oac o ou i g s a e ca ie s a e i e co ai e s o ya ucks a a e assig e o quay c a e was e e o e y Kim e a (1999 wi e o ec i e o mi imi i g e o a
a e is a ce o e s a e ca ie s e wee ya ays e o e a io o oa i g co ai e s o e s i co sis s o wo segme s S a e ca ie s i e ya a e oca i g co ai e s a oa i g em o o a ya uck Usua y ee o ou ya ucks a e e i e i g co ai e s o quay c a es a ey a e assig e o y o o e s a e ca ie Si ce co ai e s a e i e e ia e y y e a g ou co s ai s a a e o e me a e a e oa i g seque ce mus sa is y e oa i g sc e u e o quay c a es a a e o a um e o co ai e s o eac g ou a e y s a e ca ie mus e equa
o e co es o i g g ou s o e a e ya e assum io is a o e s a e ca ie is assig e
o o e quay c a e a o so e is assig me a wo-s age a go i m is o ose A e egi i g co ai e s i e ya a e i s assig e o quay c a e a e ca ie ou i g is e o me o e se o se ec e co ai e s e o em is ecom ose o se o i e e e o ems w ic a e e so e as a a s o a io o em W e se o co ai e s o a s eci ic quay c a e is e e mi e
e ou i g o e s a e ca ie s is e o me y usi g a eam sea c a go i m o a se o 9 co ai e s e ou o s a e ca ie is e e e esu s s ow a e a e is a ce o e s a e ca ie ca e e uce i e um e o ocks a e is isi i g is mi ima a a ca e ac ie e y assig i g i i i ua co ai e g ou s o sma e um e o ocks
e mo eme o co ai e s y ga y c a es a s a e ca ie s e wee e s i a e co ai e ya was s u ie y öse e a ( e sco e o e s u y was o sim i y e o e a io o e co ai e e mi a i am u g Ge ma y e cu e o e a io a s a egy is a a i e
um e o s a e ca ie s a e se ici g a si g e quay c a e us a e icie ga y c a e o e a io ca e ac ie e y e icie y sc e u i g s a e ca ie s e ga y c a es a e u oa i g co ai e s om e s i s a aci g em i e u e e ow e c a es us e co ai e is a ai a e o e sc e u e o s a e ca ie assig me W e a co ai e is ace i e u e e e e is ca e e i ime W e a s a e ca ie e i e s a co ai e o e a s e e o e s i
y a ga y c a e a ea es i i e u e is e e is a so ca e a i ime e o ec i e is o mi imi e a e ay a is e i e as ime e io e wee e i ime a e mome w e e s a e ca ie a i es o ick-u e co ai e om e u e e mo e is sim i ie y o co si e i g e s acki g o co ai e s i e u e o e e a e e o es wo ossi e assig me s a egies o s a es o quay c a es e i s o e is a semi-s a ic assig me w e e a i e um e o s a e ca ie s is assig e o a a icu a quay c a e e seco o e is a y amic assig me w e e a i e um e o s a e ca ie s ca e assig e o a y o e c a es a a e i se ice e s a e ca ie ee si e i o e a io is se o e ee imes e um e o ga y c a es e a ica io o e ge e ic a go i m is com a e o e im eme a io o e semi-s a ic a
y amic assig me s e esu s ese e e a a age o e ge e ic a go i m o e uci g
e ays i quay c a e o e a io e o a is a ce a e e y s a e ca ie s a e em y ou s a e a so e uce w i e e ime a is ee e o ocess e co ai e esse is a so e uce
O ima co ai e s o age s a egy is i co e a io wi e e o i g a o ima sc e u e o co ai e a i g equi me Ko a a es o ( mo e e a co ai e a oca io o em a mi imi es e a i g ime o a co ai e s comi g o s i s a a e same ime mi imi es e 0 co ai e a s e ime o e s o age a ea W e a s i a i es a e o e Co ai e a s e
Mo e (C M a oca es e ecessa y equi me ee e o se ice e s i e C M ies o mi imi e e a s e imes o co ai e s om e s i o e s o age a ea a ice e sa e
Co ai e oca io Mo e (C M is esig e o mi imi e e a i g ime o co ai e s i e s o age a ea e C M a C M a e i eg a e w e ese wo sys ems a e c ose e mo e s aim
o simu a eous y o imi e e co ai e a s e a s o age a i g ime e mo e so es e
wo o ems i e e e y a esu s om o e mo e a e use as i u a a o e seco mo e
A i s e C M e e mi es e co ai e a s e usi g a a om i i ia s o age oca io e e ou u e a i g sc e u e is use as i u o e C M e esu om C M is e o ima
oca io o e co ai e s a e ya a is i o ma io is use agai as a i u o C M is
ocess is e o me u i a s o i g c i e io is sa is ie So u io ec iques use a e e ge e ic a go i m a a u sea c a go i m a a y i o ge e ic a a u sea c a go i ms e ge e ic a go i m ga e e e esu s a e o e wo a go i ms I was o se e a y e uci g e ma imum s o age eig o co ai e s esu e i a e uc io i e u a ou ime
Mu y e a ( 3 c assi ie e ai y o e a io s o a co ai e e mi a i o i e ecisio s ;
ame y a oca io o e s o a i i g s i s a oca io o quay c a es o ocke s i s a oi me imes o e e a ucks ou i g o ucks is a c o icy a e e mi a ga e a e
ock s o age s ace assig me GC e oyme I a oca io o QC a I i i g a s e measu es o e o ma ce a a e usua y o imi e a e
- e a e age wai i g ime o e e a ucks a a e e i e i g ou ou co ai e s o
icki g-u i ou co ai e s
- e a e age wai i g ime o i e a a s o e ic es a a e wai i g o quay c a es o
ya equi me o oa /u oa a co ai e o/ om em
- e wai i g ime o quay c a es wai i g o i e a a s o e ic es
- e o a um e o i e a a s o e ic es a a e ei g use u i g e s i
- e um e o u o uc i e mo es a a e ei g ma e i e s o age a ea 31
e O ima e oyme o u e i e Ga y C a es ( GC i e s o age ya was
e e o e o a - ou e oyme e io Si ce e wo k oa i e ock w e e co ai e s a e s acke c a ges o e ime i was s eci ie a ma imum o wo GCs ca wo k simu a eous y wi i o e ock a ea e mo e is ase o e ayou o e o g Ko g co ai e e mi a w ic co sis s o 7 s o age ocks a as 9 GCs i se ice u i g e ay e ma ema ic mo e was ase o e a s o a io o em o mu a io a e o ec i e was o mi imi e e em y
a e ime a occu s w e GCs mo e e wee o s o e ume ica e am e e e oyme o GCs o s o age ocks was so e as a a s o a io o em a e o ec i e was o o ima y e oy GCs o ocks
e im eme a io o a ious is a c i g s a egies i au oma e co ai e e mi a s as
e goa o usi g e eas um e o equi me ee e o se e e quay c a es is a akke
( 5 e o e i e e is a c i g s a egies o e e mi e e o ima um e o au oma e gui e
e ic es (AG s ee e o se ice e quay c a es e o e a io mo e e egi s wi u oa i g a co ai e y quay c a es a i i ia mome o owe wi is a c i g o AG s is a c i g o
AG s is es e wi ou i e e s a egies e i s is a c i g u e assig s e ea es au oma e gui e e ic e o a co ai e a is ei g u oa e e seco is a c i g u e s a s wi e assig me o e a es AG o co ai e A a om assig me is use as a i me o w e e
e a go i m assig s a om y a ai a e AG o co ai e s e as is a c i g u e is a Cyc ic u e
a se ec s e i s a ai a e AG egi i g wi e successo o e as AG se ec e so a a
a a ce i wo k oa amo g a AG s is ac ie e e mo e co si e e o y i e e ces i co ai e si e ( oo a oo u i o i e e ia e e osi io o e co ai e i e s ack
e um e o AG s i o e a io was om o 3 a ou c a es we e use o u oa 5 co ai e s o com a e e e o ma ce o is a c i g u es use o assig AG s o c a es ee
a ame e s we e use ; o a cyc e ime e i e as e o a ime equi e o u oa a co ai e s o
e s i mi imum um e o AG s equi e o ac ie e a mi ima o a cyc e ime a a e age u i i a io o AG s e esu s o ai e usi g e ea es e ic e u e emo s a e a e 3 sma es cyc e ime is o ai e a ess um e o AG s is ee e o e i o e a io o e uce e
o a cyc e ime
Kim a Kim ( iscusse me o s o ou i g ya equi me u i g oa i g o e a io s i a co ai e e mi a e ya equi me co sis s o ya c a es a s a e ca ie s
a a e a s e i g co ai e s o o ya ucks ase o a e e e mi e assig me Mi imi i g
e o a co ai e a i g ime is e o ec i e o e a e is a e co si e e e case w e o y o e quay c a e is i o e a io a a si g e ya equi me is i o e a io o so e is o em
i e e a go i ms we e use A a go i m ase o y amic og ammi g e ume a e a
ossi e so u io s o i e eas cos ou e ase o e se o co s ai s e ge e ic a go i m was a seco a go i m use o mi imi e e o a co ai e a i g ime e i a go i m was a
eig o oo eam sea c a go i m a i i ia y e e o e a asic easi e so u io a e y
a c i g o e e y ai o wo co ai e s e oca io s we e e c a ge A e a o es a e e o e
e es omisi g so u io s a e c ose a a c i g co i ues om ose o es u i a o es a e co e e a e es so u io o ai e o com a e e esu s wo se o o ems we e use o es
e a go i ms i s e sma si e o ems wi co ai e s a om y is i u e i e ya we e ge e a e e a ge si e o ems co sis e o 3 ya ays wi 3 co ai e s a om y
is i u e e o ima so u io ge e a e y usi g y amic og ammi g was com a e wi so u io s o ai e om e ge e ic a eig o oo sea c a go i ms e eig o oo eam sea c a go i m a s ig y e e esu s a e ge e ic a go i m w e a e is a ces a e com a e A so o a ge si e o ems e eig o oo eam sea c a go i m ou e o me e ge e ic a go i m
Mee sma s a Wage ma s ( 1 use a a c a ou a go i m o e o em o sc e u i g a i g equi me i co ai e e mi a s wi e o ec i e o mi imi i g e makes a o e sc e u e e co ai e a i g o e a io i e e mi a is ase o a com i a io o
Au oma e Gui e e ic es (AG s e i e i g co ai e s o quay c a es a e e quay c a es a e oa i g ose co ai e s o e s i e ya equi me au oma e s acke c a es (ASC a e 33
a s e i g co ai e s om e ya o AG s i s e owe e e ou s a e ou a ey a e use o isca e o es a a e o gi i g e e so u io s W e e e e um e o o es a a e
ei g e a ua e e ci es 1 5 e a go i m s o s a e cu e es so u io is use as a o ima so u io e seco eu is ic use is a eam sea c a go i m a is e a e o e a c a ou a go i m e um e o co ai e mo es a a e ei g co si e e a ies e wee a 1 a
ey a e ei g a e y a ma imum o 7 au oma e s acki g c a es a AG s e um e o c a es i o e a io a ies e wee a e esu s om o a go i ms a e com a e o
i e e o em si es a e esu s y o a go i ms a e simi a e com u a io a ime o e
a c a ou a go i m is 1 o imes o ge a ime o e eam Sea c a go i m
oy a e a ( e e o e a mo e a i co o a es mo e a ia es i o simu a i g e o e a io o a co ai e e mi a so a e simu a io ca e ese e e ea wo o e a io s e
a a use is a is o ica 3 ay o e a io a a o ai e om o o a y oca e sou -eas o
Sy ey ai mou e ga y c a es ( MG a e a i g co ai e s a mo i g em o a i e me ia e s o age a ea o quick a s e o ucks o ai s e co ai e a i g wi i e
e mi a is e o me wi s a e ca ie s e o ec i e is o i a sc e u e a ou e o MGs
a o imi es ei u i i a io w i e mi imi i g e s a e ca ie ee si e e e cess equi me ca e u i i e i o e sec io s o e e mi a o co ai e a i g e e mi a a ea is i i e i o ee sec io s e Ga y- oa I e ace co sis o uck s o s a wo ai way acks o
a s e o co ai e o o om ai s a ucks is a o e e mi a was o a o e o imi a io e Ga y-S a e i e ace is e ya a e use o s o e e o co ai e s a a e
e i e e u o 1 ou s e o e s i a i a ime is i e ace is a o e o imi a io ocess a
e o imi a io is e o me y ai i g mo es e i e me ia e S acki g A ea is oca e e wee
wo o e i e aces a i is use o s o age o im o co ai e s a i e y ucks o ai s a
ese co ai e s a e e mo e om is a e o s i s a o e ai s e o e a io wi i is
e mi a is e o me y semi-au oma e ai mou e ga y c a es o sim i y e mo e e si e o co ai e s a y e ( ee e co ai e s wi a ge ous goo s e c o co ai e s a e o ake i o 3 co si e a io e o imi a io was i i e i o ee o ems A e i i ia s e a s a egic i ege
og am es ima es e mo eme ime o co ai e s e wee e i e me ia e s acki g a ea a ga y s a e i e ace y e e mi i g e sc e u e Eac co ai e as o e mo e wi i e s eci ie ime wi ow a a i ege og am mi imi es e um e o s a e ca ie s ee e o e i o e a io u i g eac ou A so e u i i a io o e i e me ia e s acki g a ea (ISA as o e wi i i s imi s e esu s a e e u i i a io o ISA e um e o o e a io s o e ai mou e ga y c a es we ime o co ai e s a e ISA I was e e mi e a se e s a e ca ie s a e
ee e o o ima o e a io A e seco s e e i ege og ams a e use o so e e osi io o co ai e s a e ga y-s a e i e ace (GSI a a e i s e a mo e ase o e o i e a go i m assig s ai mou e ga y c a es om e Ga y ai I e ace o co ai e s om ga y-s a e i e ace e esu s s owi g e u i i a io o e ai mou e ga y c a es a i was i ica e a e a ou 15 o e o a mo es a e em y mo es a 7 5 i o e oa i g a u oa i g o e a io s A so 3 o e ucks e e ie ce a e cessi e wai i g ime si ce e a i a a e o ucks was ig a c a es cou o a e em i ime as i i a sa g (1999 w o e a sys ema ic e iew o o ems associa e wi a co ai e
e mi a e ocume esc i es i e i e e sc e u i g o ems a ecisio s a a e o e ma e e i s o em a a y e is e e a oca io o s i s a quay c a es a oca io o ocke s i s wi e goa o mi imi i g s i wai i g imes a ma imi i g e o s u a ou e seco ecisio is o e e mi e e s o age s ace i e s o age a ea o co ai e s so a
eo ga i i g a es u i g o co ai e s is mi imi e w ic ea s o mi imi i g e cos e co ai e e i o em ea s wi u e y e Ga y C a e ( GC assig me i e ya
a a ec s quay c a e e o ma ce a e e o ma ce o e ic es a a s o co ai e s as we
e ou ecisio a as o e ma e is sc e u i g e ic es a a s o co ai e s e wee e ya a quay a e o ec i e is o mi imi e e a s o a io cos a wai i g ime o quay c a es a GCs e i a o em ese e is e ocessi g o e e a ucks a i i g o ick up or deliver containers. The goal is to minimize the waiting time of trucks and congestion at the gate,
For each of these problems the objective function and constraints that have to be met are presented,
Tsang (1994) wrote a review of scheduling techniques that can help a problem solver to better understand which scheduling methodology should be used based on the objective of the problem.
Basic questions are proposed, whose answers can help the problem solver to choose the appropriate scheduling algorithm. Some of the scheduling techniques such as linear programming, branch and bound, tabu search, genetic algorithm etc. are explained and the type of scheduling problems they can be used are listed in Table 5.1.
bl . Considerations in Choosing Among Major Scheduling Techniques Major technique specific General Considerations considerations
Used for optimization with linear Problem must be specified by a Linear functions Programming (normally conjunctive) set of Intractable inequalities Used for optimization Require heuristic for pruning Branch & Ordering of branches is Bound Intractable important
Effectiveness mainly depends on Tabu Search strategy on tabu-list manipulation
Useful for frnding near-optimal Representation is crucial solutions Genetic Effectiveness could be sensitive to Algorithms Requires nontrivial time, but choice of parameter values and hopefully will search a wider operators part of the solution space 6
.2 C nt n r r n l Op r t n, n nd S l t n
A c assi ica io a i e a u e e iew o co ai e e mi a o e a io s a e ee o i e y
S ee ke e a ( e au o s i i e e e iew i o s i a i g ocesses s owage a s acki g ogis ics a a s o a io o ems I ei c assi ica io e i s ca ego y co sis s o
e a oca io e s owage a i g ocess a c a e a oca io e ecisio s e a e o ya c a es a s o age a ea a oca io a e i e seco ca ego y e i ca ego y o e
ecisio s e e s o a s o a io o ems om e quay si e o e s o age a ea o ice e sa e co ai e a i g equi me mo eme om ei sou ce o ei es i a io a o a c i si e e e mi a om e ogis ics oi o iew a gai i s i o uc i i y ca o e
ecessa i y ac ie e y i c easi g e um e o equi me a a e se ici g e c a es si ce a mig c ea e co ges io us e o imi a io sys em as o e e e o e a ea s wi mi imi i g co ges io ocessi g o ucks is esc i e as a y amic sys em a c a ges i ime ue o
e ma e y c a gi g a ic o ume e o imi a io as o e e i e a as a o i e o imi a io is a ossi e ec ique a ea s o goo esu s I is o em mi imi i g em y
is a ces a e a e imes a e e mai ocus o o imi a io a is ca e ac ie e y com i i g a s o o e o co ai e s om e ya o ucks wi im o co ai e s a a e ake o om e ucks a s o e a e ya
Sgou i is a A ge i es ( e e o e a simu a io mo e a simu a es e a i g o i ou co ai e s y s a e ca ie s e mo e as e c a ac e is ics o essa o iki s co ai e
e mi a w e e ya equi me a a es co ai e s co sis s o s a e ca ie s o y e co ai e s a e u oa e om e s i y quay c a es a s acke i e im o a ea y s a e ca ie s a a e o y assig e o c a es e ucks a e u oa e y s a e ca ie s assig e o e im o a ea a ose co ai e s a e a s o e o e e o a ea y e same s a e ca ie s e simu a io mo e gi es i sig s o ow im o a ea u c io s y ca cu a i g equi me u i i a io and truck turnaround time. The model estimates the required number of straddle carriers to ensure an acceptable level of service and it demonstrates the benefits of implementing an automated container management system. The software used in developing the models is called "Extend" and it simulates discrete event problems. As input variable characteristics of the stacking yard, shift pattern, yard filling rate, arrival distribution for trucks depending on load status, straddle carrier operational parameters, duration for system operations and information regarding general port organization methods are used, The output of the simulations is generated as a report that contains average queue time, total time, cycle time, average service and wait time, utilization of the straddle carriers, number of trucks processed per shift, etc. The truck arrival times are assumed to follow Erlang distribution with m=2 and k=2.The Erlang distribution is also used for the inter arrival times in the server-client system. A validation of the simulation model was performed based on real world information. The historical data for truck arrival times for each day was used an input. A straddle carrier utilization of
70% was recommended by manufacturers for the cost effective utilization of equipment.
Behera et. al, (2002) discussed the terminal yard operations and developed a simulation model to examine the impact of the straddle carrier fleet size on the terminal overall throughput. The researchers compared two different job assignment rules. A service based on the frrst-in-first-served principle was tested against a simple heuristic that assigns each job to the closest available straddle carrier, The simulation results revealed that both the old and new rules performed equally well using performance indicators such as average container flow time, daily throughput, average waiting time of jobs, number of jobs in the queue, and straddle utilization,
. A n nt Al r th
The problem of assigning equipment, personnel etc. asks for the best assignment of a set of machines and people to a set of jobs, Kuhn (1955) developed a methodology to answer the question of 38 assigning n individuals to n jobs that maximizes the benefit and no job is assigned to two different individuals.
The general assignment problem consists of the choice of one job, from the set of n available jobs (j=1, 2,. . . , n), and for each individual from the set of individuals n (1=1,2, .., ,n), such that only one job is assigned to one individual, Given an n by n matrix R=(rij) of positive integers, find the permutation j1 , . . , j of integers 1, 2, , , . , n, the assignment maximizes the sum r1 j + + r j , A liner dual program is then used to transform this problem to a minimization problem that is finding non-negative integers u 1 , . . . , u and v1 , . , . , v subject to
ui + ≥ i (i =1 (1) that will minimize the sum u1 + + u + 121 + + v
To solve the minimization problem, the algorithm is using two basic routines called Routine I and Routine II and on Figure 5,1 the order of their repetitions is given.
Figure 5.1 Schematic description of the order of repetition of routines
A set of non-negative integers that satisfies the constraint (1) is called a cover and the position (i, j) in the matrix for which equality holds is said to be marked; otherwise it is said to be 39 blank, A set of marks is called independent if there are no two marks that lie in the same line.
Routine I is associated with a fixed cover tu , vi } , The basis for Kuhn's Algorithm is outlined in the following form:
Step A. Subtract the smallest element in matrix R from each element of R, obtaining a matrix R 1 , with non negative elements and at least one zero.
Step B. Find a minimal set of lines S i , n1 in number, which contains all the zeros of R 1 , If n1 = n there is a set of n independent zeros and the elements of R in these n positions constitute the required solution,
Step C, If n1
Step D. Repeat steps B and C using in place of R 1 , The sum of the elements of the matrix is decreased by n(n-nk)hk in each application of Step C, so the process must terminate after a finite number of steps.
Munkres (1975) developed a variation of Kuhn's algorithm for the assignment and transportation problems, The algorithm is used for the allocation of ships so that one location has a specific number of ships and at the the cost of moving ships between locations is minimized, The problem statement is: There are N ships placed at positions P I , . . n and ri denotes the number of ships at position Pi. If somebody wants to move ships to a new position Q1, . . . , Q., so that there will be c ships at position Q , the number dij is the cost of moving a ship from position Pi to position Q .
The number x1 stands for the number of ships that will be moved to a different position and it will be called the quota assigned to The problem is to choose these quotas so that the total cost of moving the ships is as small as possible, Such a choice of quotas is called an optimal solution of the assignment problem The solution is obtained by using Steps A, B, C and D from above.
Kumar (2006) proposed a modified method for solving the unbalanced assignment problem where the number of jobs, m, is larger than the number of available workers, n, For the initial matrix 40 that is generated, the sum of rows and columns is calculated and sorted in ascending order, Two matrices are then created. The first matrix is a square (nxn) matrix that can be solved by the
Hungarian method and the result is an optimal assignment. Starting from the first smallest value of row sums, corresponding row from the original matrix is a new row in first matrix. This approach is repeated until n new rows are generated, The second matrix represents the assignment of the workers to remaining jobs and can be interpreted as the assignment of fictitious workers to machines. The minimal value in the sums of columns is selected and corresponding column is selected to generate the first column of the second matrix, The process is repeated until m-n columns are selected.
In real life situations it is difficult to balance jobs and machines, Not always we have the case that the number of available persons is same as the number of jobs that has to be completed. In the case of unbalanced assignment problem the literature suggest adding fictitious jobs or workers to obtain the balanced assignment problem. Solving this problem results in an assignment of some jobs to fictitious machines or persons. This fictitious assignment can be ignored in the final result.
.4 S r
The literature review provided the knowledge of how the complex systems in the port operates and what should be the right way to analyze the operation of straddle carriers. Studies by Das and
Spasovic and Steenken provided the groundwork for the investigation of processes, methodologies and objectives associated with equipment assignment in the port, and they will be used as the basis for the algorithms that will be used in this dissertation. Although there are a significant number of studies done on the topic of assigning equipment in container terminals, the literature review did not present any attempts of postponing decisions regarding dispatching equipment with the goal of improving service that this research explores. The literature review provided knowledge regarding the objectives of terminal management and what are the important decision variables in evaluating operations. The review of different assignment algorithms and heuristics concluded that there is no single best algorithm that gives the best results, C A E 6
ASSIG ME MO E S O S A E CA IE SC E U I G O EM
Many of the problems that management faces in designing logistics operations can be associated to some general classes of transportation and network routing problems. Operations optimization using methods of operations research in container terminals has become imperative, since the operations are very complex and further improvements can be made using scientific methods,
This chapter defines the assignment models that will be used to a case study in attempt to answer the research question stated in Section 4,3. The assignment logic and scheduling methodology will describe the continuous time dependable assignment process of straddle carriers to trucks.
6. Obj t v n t n n A n nt r bl
The literature review identified different objectives in solving the problem of assigning and scheduling equipment in a container terminal. The objective of the straddle carrier assignment problem can be either minimizing truck waiting time, minimizing the total straddle carrier travel distance, or minimizing the total distance that straddles traverse without carrying loads, Also, it is possible to have a combination of different objectives. The objectives are given priority based on a weight, A sample objective function is shown bellow.
Objective Function = a * straddle distance travelled + (3 *delay in servicing trucks
Where: a is a travel cost per unit distance for straddle carrier
is a linear cost penalty for delay in servicing a truck
4 42
The objective function used in this dissertation is to minimize the total distance traveled by straddle carriers, defined as the sum of distances that straddle carriers need to travel to (from) trucks and pick-up (deliver) containers and the distance that carriers need to traverse in order to drop (pick- up) containers to (from) the yard,
6.2 h Str ddl C rr r A n nt M d l
The following algorithms and heuristics are used:
- Heuristic based on the First Come First Served (FCFS) Rule: The straddle carrier is assigned
to the first truck entering a slot in the yard
- Heuristics based on the Hungarian Algorithm
- Heuristic based on Implicit Enumeration
6.2. r t I. Appl t n f th r t C r t S rv d ( C S l
As soon as the first truck enters a slot, the straddle carrier that is closest to it is assigned to service it.
This is the first come first served rule (FCFS). The algorithm assignment procedure is presented in
Figure 6.1.
As the truck enters the slot, the algorithm examines which straddle carrier, from the pool of available carriers, is closest to the truck. If all straddle carriers are busy as the truck enters the slot, the algorithm calculates which straddle carrier is going to be available first, and then it assigns that straddle carrier to the truck in the slot, The algorithm follows this logic until all trucks are processed. 4
r 6. Assig me ogic ase o C S u e
6.2.2 n r n Al r th f r Str ddl C rr r A n nt
e e e o me o is a go i m was mo i a e y a i ea a i o e ca a i io e co i uous a i a o ucks u i g a ay i o a seque ce o ime ames o ce ai u a io a i e o ima s a ic assig me ca e ma e wi i eac ime ame e e esu i g a oac wi yie a o ima assig me o e e i e o e a i g ay o is e we ee o i e o ima ime o se
om e mome a uck is s o e a u i i is assig e o a s a e o e a o a e o e a go i m e o owi g e mi o ogy a o e a i g u es ee o e i o uce
e mi o ogy a O e a i g u es
- o e o es e i e oi i ime a w ic e a go i m ca cu a es e assig me o s a e
ca ie s o ucks
- e a i g e io is e i e as a ime i e a om e mome w e e i s uck e e s
e s o u i ime o w e e i s s a e ca ie is is a c e (e g o
- u i g e a i g e io e s a e ca ie s a e o assig e o a i i g ucks
- e o e ecu io e io is e ime i e a ( OE u i g w ic e s a e ca ie s a e
se ici g ucks
e ques io a ises as o ow o ea e acce a e imi o uck wai ime wo i e e a oac es a e use o is ey a e iscusse i u
1 u ga ia A go i m I - o imi s o uck Wai ime
e e e s o e o e a io o w ic e a go i m is e e o e a e s ow o igu e ucks a e a i i g a e s o s u i g a ce ai e io is e io is ca e e a i g e io a is esig a e
y e [ o] ime i e a e ucks a e a e o a uck queue A e e o e a i g e io a ime a assig me o s a es o ucks is ma e e s a e ca ie s a e assig e o ucks
y usi g e u ga ia Assig me e esu i g s a e o s a e e ecu e u i g e o e ecu io s age esig a e as e [ o e io I a s a e ca ie i is es i s assig me e o e e ime
e io e s ( i wai s u i e e is a c i g sc e u e is ma e a ime (
e e g o e a i g e io wi e a ie a e o ima so u io i e i ie e so u io wi e eas o a a e is a ce wi e ec a e as e o ima a e esu i g o ima s a e ca ie ee si e a e a i g e io u a io o e 4
r 6.2 Planning period and assignment process
A n nt
The decision process is described as follows:
St p . Determine which straddle carriers are available to be assigned to trucks waiting in the
queue, For the initial assignment at T o, all straddle carriers are available and the algorithm
proceeds to Step 2. If the straddle carriers are not available, wait until the planning period expires
and go to Step 4.
St p 2. Calculate an optimal assignment of straddle carriers to trucks that minimizes the total
distance travelled by each straddle carrier-truck pair. Use the Hungarian Algorithm to obtain the
optimal solution. Calculate the job execution time, namely the time when the straddle carriers will
become available for the next assignment,
St p . Carry out the assignment - dispatch the straddles to the trucks. Add any unassigned trucks
to the unassigned truck queue. Wait until the end of the job execution period, 46
Step 4. After the job execution period has expired, examine the truck queue (The queue consists
of unassigned truck from the previous planning period and newly arrived trucks (those arrived
during the job execution period), Proceed to Step 1.
The above assignment Logic is presented on Figure 6,3
Figure 6.3 Logic for Hungarian assignment without the limit on truck wait time
6.2.2.2 Hungarian Algorithm II — Truck Priority Rule
This algorithm addresses the issue of excessively large waiting times that can occur as a result of the previous algorithm. It accomplishes this by giving priority to the trucks that can be processed during the same planning period. This means that a straddle carrier will be assigned to those trucks that can 4 be processed in the shortest possible time before the planning period ends. If the time required to process a truck is longer than the remained of the planning period, then the truck will not be processed,
If a straddle carrier finishes its assignment before the time period ends (2T ), it will evaluate
if it can make another assignment until the next dispatching schedule is made at time (2T ). If the assignment is possible, a straddle (or set of straddles) that has accomplished its job early is assigned to the trucks in the queue.
The events of the operation shown on Figure 6.2 are also used here, However, in marked contrast to Figure 6.2, the events in Figure 6,4 allow for the straddles to be reassigned to trucks for jobs that can be performed before the start of the next planning horizon. Namely, the assignment is carried out if it can be completed during the [T OE, 2T ] period.
This change in the assignment is shown in the algorithm steps listed in Figure 6.5.
r 6.4 Modified assignment process 48
Al r th
e a go i m is as o ows
St p . e a go i m e e mi es i s a e ca ie s a e a ai a e o e assig e o ucks wai i g
i e queue o e i i ia assig me a o a s a e ca ie s a e a ai a e I e s a e ca ie s a e o a ai a e ocee o S e 5
St p 2. Ca cu a e a o ima assig me o s a e ca ie s o ucks a mi imi es e o a
is a ce a e e y eac s a e ca ie - uck ai Use e u ga ia A go i m o o ai e
o ima so u io Ca cu a e e o e ecu io ime o e e mi e e ime w e e s a e ca ie s wi ecome a ai a e o e e assig me
St p . Ca e assig me e accom is e e o e e e o e a i g e io ? I yes ca y
ou e assig me — is a c e s a es o e ucks Go o S e O e wise go o S e 5
St p 4. A e e e a y ucks om e e ious a i g e io s i wai i g i e queue? I yes go o S e
St p . Wai u i e a i g e io as e e E ami e e uck queue e queue co sis s o u assig e ucks om e e ious a i g e io (s a e ew y a i e ucks u i g e as e io ocee o S e 1
is assig me ogic is s ow i igu e 5 o e e age 4
r 6. Logic for the Hungarian algorithm with priority assignment
6.2. r t th I pl t En r t n
The heuristic presented in this section uses a different approach in assigning straddle carriers than the previous heuristics. It determines a sequence of jobs that each straddle carrier in the fleet has to perform with the objective of minimizing the total distance travelled. It then develops a schedule which assigns a straddle carrier to a truck sequence. 0
A n nt r d r
r n l nd Op r t n l
- o e o es e i e oi i ime a w ic e s a e ca ie s a e is a c e o ucks
- e a i g e io is e i e as e ime i e a a as om e mome w e i s uck
e e s e s o u i e ime o w e e i s s a e ca ie is is a c e
- u i g e a i g e io s a e ca ie s a e o assig e o ucks a a e a i i g
- e o e ecu io is e ime i e a ( o u i g w ic s a e ca ie s a e is a c e
a assig e o ucks ase o e assig me e e mi e u i g e a i g e io
- e ma imum a owe wai i g ime w e o es e ime a is a owe o a uck o wai
u i i as o e assig e e uck a as eac e e ma imum wai ime wi e assig e i e e a i g e io
- e co o ime C e o es a oi i ime w e e a go i m c ecks i a y uck a is o
assig e a i is i e u assig e uck queue as ee wai i g o mo e a e ma imum a owe wai i g ime
e s a e ca ie s a e wai i g o e e i e u a io o e i i ia a i g e io (
e o e ey a e assig e o ucks A ime e assig me o s a e ca ie s o ucks is ma e e s a e ca ie s a e e is a c e o ucks
e assig me ocess is s ow i igu e e s a e ca ie s ca y ou e assig me o e u a io o e o e ecu io e io I a s a e ca ie i is es i s as assig me e o e e a i g e io e s ( i wi wai u i e e is a c i g sc e u e is ma e a ime ( I e s a e ca ie is usy w e e e is a c i g sc e u e as o e
e e mi e i wi wai u i e e is a c i g mome O y ose s a e ca ie s a a e a ai a e wi e is a c e o ucks i e queue 51
A a e e e mi e ime (Ct e o e e o e ecu io comme ces e a go i m wi c eck i a y uck i e queue as ee wai i g o mo e em e ma imum wai i g ime wt a i i as
is uck wi e gi e io i y i e e s a e ca ie is a c i g cyc e
o is assig me e ma imum wai i g ime w t was se o 1 mi u es e a go i m a e
e o ima sc e u e as ee e e mi e ee ami es e sc e u e ase o ow o g e ucks a e wai i g o se ice I e uck as ee wai i g mo e a 1 mi u es i is mo e o e egi i g o e queue a io i i e I mo e a o e uck is e e mi e o e wai i g mo e a 1 mi u es
ey a e mo e o e egi i g o e queue a a e se ice ase o w o as wai e o ge
eu is ic ogic
e ocess o e e o i g a sc e u e o assig i g ucks o s a e ca ie s co sis s o e o owi g s e s
Step I. Develop an optimal schedule for one straddle carrier that can process all trucks in the fastest manner. o eac a ai a e s a e ca ie e e mi e e uck seque ce w ic mi imi e e
o a a e e is a ce e uck seque ce is e e mi e y usi g e s o es a a go i m e 5 s o es is a ce a si g e s a e ca ie as o a e se o ocess a ucks is o e as e i i ia so u io a e seque ce as e i i ia sc e u e
Step 2. Introduce a second straddle carrier in the operation. om e se o emai i g s a e ca ie s a i a i y se ec a seco s a e ca ie o e i o uce i o e a io e a go i m is
aki g o e uck om e i i ia so u io a assig i g i o e ew s a e ca ie e o a
is a ce a e e y o s a e ca ie s is ca cu a e a com a e o e i i ia so u io o ai e i S e 1 I e ew o a is a ce a e e is sma e a e i i ia so u io e uck is e
e ma e y assig e o e seco s a e ca ie e ew o a is a ce a e e is e ew so u io I e seco s a e ca ie was a e o im o e e so u io y aki g o e uck i ow
ies o ocess a o e uck so a e o a is a ce a e e is u e e uce (e g sma e a
e ew so u io ou i S e I e a go i m i s a o e uck a ca e assig e o e seco s a e ca ie i o e o u e e uce e o a is a ce a e e i wi o a
S e 3 om s e s 1 a e assig me o wo s a e ca ie s is e e o e o ocess a ucks so a e o a is a ce a e e is mi imi e e a go i m i is s e wi e i y i e e is a o e s a e ca ie a ai a e I e i s a e ca ie ca o im o e e so u io e a go i m wi "i o uce" e e s a e ca ie s i a ai a e e ocess wi co i ue u i e a go i m i s e e s a e ca ie a ca im o e e so u io
I e a go i m ca o im o e e so u io a a s a e ca ies we e i es iga e e
e assig me o s a es o ucks is e o ima seque ce o o s a wi mi imi e e o a is a ce a e e
o e e y a ai a e s a e ca ie e ocess o aki g o e a o om e e ious y assig e s a e ca ie s is e ea e A e e e o ima assig me o a ucks o s a e ca ie (s is e e o e 53
The flow chart for the Heuristic with Implicit Enumeration is presented in Figure 6.7,
igu e 7 Logic of the Implicit Enumeration Heuristic CHAPTER 7
THE APPLICATION OF STRADDLE CARRIER ASSIGNMENT MODELS
e e ious c a e ese e i e e a go i ms a eu is ics a wi e use o e s a e ca ie assig me o em is c a e ese s e co ai e e mi a ayou e uck a i a
a e a e a icu a s o e Case S u y o w ic e a go i ms a eu is ics wi e a ie
e o ima o e a io i e ms o e a i g e io u a io mi imum is a ce a e e a e
e a e mi imum o ima ee si e wi e i e i ie
7.1 Case Study
7.1.1 Terminal Layout and Data Description
e Case S u y co sis s o o e a io a a a om a o o ew Yo k/ ew e sey co ai e e mi a
o eac uck a is a i i g a e e mi a ( o a ick u o o o e o owi g i o ma io is a ai a e
- A i a ime a e ga e
- S o osi io a e ime e uck a i es a e s o
- Co ai e oca io i e ya ( o w ic e co ai e ee s o e o e o o om w ic
i ee s o e icke u
e ayou o e co ai e e mi a is i us a e i igu e 7 1 is e mi a co sis s o ee
o es co ai i g s eci ic co ai e ocks Eac o e co sis s o se e a ocks a ocks a e
o me om a g ou o ows I eac ow co ai e s a e s ack u o ee ig Eac s o age oca io
as a u ique i e i ica io a ess (e g M 7 7 e c w ic esc i es e osi io o e co ai e
54 55
The trucks slot area is where containers are transferred from a truck chassis to the straddle carrier and vice versa. There is a finite numbers of slots. There is a limited number of straddle carriers to serve these slots, Each truck is assigned to an empty slot, and when the truck enters the slot it is ready to be served by a straddle carrier.
Container Blocks
igu e 7 1 Terminal Layout
The data consist of 495 truck arrivals at the terminal and their arrival pattern is shown in
Figure 7.2. The assignment is performed with a fixed straddle carrier fleet size varying from 3 to 8,
The assignment models are written in the Visual Basic programming language. The programs are using as input a text file that contains truck arrival and container location information. The output results for each assignment and the related summary is saved in an MS Excel file. 5
igu e 7 uck a i a is i u io e ou o o e a io
7 1 Assum io s
e osi io o a co ai e s a e ca ie a uck a e e mi a is e i e y i s " " a "y" coo i a es e s a e ca ie a e a e wee a y wo oi s is assume o e ec i i ea a
e ce i e o a e i e i A co ai e mo es ei e om e s o age oca io a e ya o e s o a ea o ice e sa
e s a e ca ie s a e oca e a e o igi oi a e egi i g o ei assig me
e io e um e o s a e ca ie s a ai a e a a y ime is k ow a ei oca io is a so k ow e e a e o a om eak ow s a ey a e a e i g a co s a s ee o a m ( o
a ic im ac s ei mo eme a acce e a io / ece e a io imes a e o co si e e
e uck wai i g ime is ca cu a e om e ime e uck e e s e esig a e s o a e
ime a s a e ca ie egi s o se ice i e oca io o a co ai e i e s ack oes i ue ce
e o -o o ick u ime 57
o com a e e i e e assig me s a egies e o owi g e o ma ce measu es a e ca cu a e
- Distance Travelled by Straddle Carriers. e o a is a ce a e e is ca cu a e as e sum o a a e i i e a ies a s a es com e e u i g e o e a io
- Average Service Time. o ucks a a e icki g u a co ai e e a e age se ice ime is
ca cu a e as e ime i e a om e mome w e s a e ca ie is assig e o ocess
a uck u i a co ai e is o e o e uck c assis o e uck a is i gi g a
co ai e i o e o e se ice ime is ca cu a e as a ime i e a om e mome w e
e s a e ca ie is assig e o e uck u i e co ai e is emo e om e uck c assis
- Average Waiting Time per Truck, is a ame e is ca cu a e as e ime e a se om e mome a uck is s o e u i i is assig e o a s a e ca ie
- Completion Time f0r All J0bs. e com e io ime is ca cu a e as e ime e a se om e i s uck a i a u i e as s a e ca ie as i is e wi i s assig me
o eac eu is ic e e o ma ce measu es a e ca cu a e a eu is ics wi e com a e
o eac o e o e e mi e e es a oac o eac i g e o ec i e o e isse a io e i i ia so u io e ase i e a go i m wi e ase o C S u e a o e im eme e a go i ms wi y o im o e u o is so u io
7 Case S u y esu s
7 1 i s Come i s Se e
e o ima esu s o a gi e s a e ca ie ee si e a e s ow i a e 7 1 e e o ma ce measu es a e ese e as ows a e s a e ca ie ee si e is s ow as co um s 8
bl . esu s o e A ica io o C S u e
Str ddl C rr r l t S z
4 6 8
A e age Wai i g ime e 51 15 5 11 1 1 3 uck ( mm ss A e age Se ice ime e 5 1 1 3 9 1 5 1 3 3 9 uck ( mm ss A e age Se ice ime e 1 7 39 1 17 3 9 9 1 39 o -O uck ( mm ss A e age Se ice ime e 1 1 13 1 5 9 3 3 ick-U uck ( mm ss ime ee e o ocess a 1 1 1 13 1 1 13 1 1 11 17 1 11 17 1 11 17 o s ( mm ss
o a is a ce a e e y 51 5 3 3 97 5 7 59 7 S a e Ca ie s (mi es
.2. . n f lt
om igu e 7 3 i is a a e a e o ima so u io i e ms o mi imi i g e s a e a e
is a ce is o ai e y u i g se e s a e ca ie s e o a is a ce a e e y se e s a e ca ie s is 5 7 mi es e a e age uck wai ime is 1 seco s e ime e a se u i a ucks a e ocesse is 1 ou s 11 mi u es a 17 seco s 59
igu e 7 3 o a is a ce a e e as a u c io o s a e ca ie ee si e
e ime ee e o ocess a ies y ess a mi u es w e mo e a ou s a e ca ie s a e i o e a io ( a e 7 1 is mea s a i e s a e ca ie s may e su icie o
ocess a ucks o ime W e i e i s ea o eig s a es a e use e uck e ay wou i c ease om 1 seco s o mi u es 11 seco s w ic i e a i e e ms may o e muc o a
i e e ce w i e e cos o "sa i g" wo s a es may e sig i ica e o owi g ques io o e
a e-o e wee se ice qua i y o ucks a e uce ca i a cos o e o e a o ca e os u a e
y e o e a o "W a is e s a e ca ie ee si e a I ca u a mi imi es my cos w i e kee i g a sa is ac o y e e o se ice o ucks i e ms o acce a e se ice ime?" I ca e see
om igu e 7 (a a e 7 1 a e a e age uck se ice ime w e i e s a e ca ie s a e o e a i g is a ge y a mos mi u es com a e o e a e age se ice ime w e se e s a e ca ie s a e i o e a io
e c a ge i a e age se ice ime e uck a a e age wai i g ime e uck as u c io o s a e ca ie ee si e is s ow i igu es 7 a 7 5 es ec i e y
igu e 7 A e age se ice ime e uck
igu e 7 5 A e age wai i g ime e uck 6
.2. .2 h An l
A i e es i g a oma y is e ea e i igu e 7 3 I e o e a io is ca ie ou wi eig s a es a e o e s a e is emo e o e wou e ec a e emai i g s a es wou a e
o ge is a ces o accom is e same amou o wo k I ac e o osi e is ue emo i g a s a e e uces e o a is a ce a e e is esu ca e e ai e i e o owi g way
a i g mo e s a e ca ie s i o e a io a i is ee e oes o esu i e o ima o e a io
u e mo e e o osi e is ue — a i g mo e s a es a i is ee e wi esu i a i e icie o e a io e oo cause o is i e icie cy is a u coo i a e com e i io amo g e s a es o
ucks (co ai e s I is com e i io e s a es a e s ea i g oa s om eac o e us
es oyi g e ma c i g o o u i ies a wou ea o e mi imi a io o a e e is a ce e
emo a o a s a e i c eases e c a ces o a ma c as i is s ow i e e uc io i mi eage
Simi a y y a i g mo e a ai a e s a es a ee e i mea s a a s a e may e assig e o a uck w ose se ice ca ea o a o ge a e is a ce ea i g e uck o wai a
e assig e y a i e e s a e a a a e ime may esu i a e e assig me i e ms o e uce is a ce a e e
.2.2 n r n A n nt I lt
e e ious sec io a u e o e ac a y os o i g e assig me o s a es o ucks a
a gi e sa i gs i e ms o e uce mi eage a e e ca e ac ie e u e mo e a u e sa i gs i s a e ca ie ee si e ca e o ai e wi ou a sig i ica e e io a io i uck se ice
ime e sou ce o is e icie cy is e o e i is a go i m
.2.2. n f lt
e esu s i igu e 7 s ow a e o ima so u io is o ai e w e i e s a e ca ie s a e i o e a io a e a i g e io is se e mi u es
igu e 7 o a is a ce a e e y s a e ca ie s
e a e age uck wai i g ime s ow i igu e 7 7 i c eases wi e u a io o e
a i g e io As mo e ucks a e a i e a a e i e queue e e a e mo e o o u i ies o s a e ca ie s a e e ( eas is a ce a e e ma c es o s a es wi ucks ca e ma e i
ewe s a es a e a ai a e o e assig me Si ce e e is o imi o e ma imum a owe wai i g ime s a e ca ie s o o a e a y io i y assig me s ase o e wai i g ime co s ai e ack o e co s ai esu s i e cessi e wai i g imes o some ucks ecause ey mig e a away i e ya a ca o y e assig e i e e is a a ai a e ca ie a oes o
a e a y o e ( e e ucks o c oose om 3
igu e 7 7 A e age uck wai i g ime o se ice
igu e 7 s ows a e o a ime equi e o ocess a ucks is a so a u c io o e
a i g e io As e a i g e io e g is ge i g i c ease e a e age wai i g ime o
ucks is a so i c ease S a e ca ie o a o e a io a ime is s a i g o i c ease a a ig e a e
o ce ai a ues o e a i g e io
igu e 7 ime e a se u i a ucks a e ocesse
7 e A oma y emai s
e a oma y e cou e e i Sec io 7 1 wi e eu is ic usi g e C S u e c ea y occu s i is case as we a i g a a ge um e o s a e ca ie s i o e a io is esu i g i a wo se so u io i e ms o e o a is a ce a e e o i us a e is oi a i g ee s a e ca ie s a a ou - mi u e a i g e io yie s a s o e o a a e is a ce com a e o e case o eig s a e ca ie s is a c e o e same a i g e io I e a i g e io is e wee eig a 1 mi u es e o e a io wi se e s a e ca ie s as e e esu s i e ms o is a ce
a e e a e o e a io wi eig s a e ca ie s
7 3 u ga ia Assig me II esu s
e esu s a e s ow i igu e 7 9 e o ima so u io is ac ie e wi s a e ca ie s a a
i e- mi u e a i g e io
igu e 7 9 o a is a ce a e e y s a e ca ie s 65
Should the terminal operator decide to run the operation with one less straddle and with the planning period of nine minutes, the total distance travelled will increase marginally by only 0.265% or 1.25 miles. This will be accompanied by insignificant increase in average truck waiting time (from 5:01 minutes to 5:08 minutes) as shown in Figure 7.10.
If the terminal operator would like to reduce the waiting time of trucks even more, he can operate with seven straddle carriers with a five-minute planning period which will reduce the truck waiting time by 25.91% (reduction of waiting time from 5:01 minutes to 3:43 minutes). This will result in an increase in the total distance travelled by only 1.296%.
Figure 7.10 Average truck waiting time for service
The average time that trucks wait for service does not differ substantially, if there are seven or eight straddle carriers in operation. Waiting time reaches the minimum of 3:10 minutes for eight straddles in operation and for a 5-minute planning period. That is 36.88% less compared to the waiting time when the total distance travelled reaches minimum.
Figure 7.11 shows the time required to process all trucks. The time elapsed is significantly different only when three straddle carriers are in operation. It shows that the operation has significant delays in processing trucks. As the planning period increases to more than six minutes, the time required to process all trucks is the same for four straddle carriers and above, This means that extending the work hours at the terminal to process all trucks is not required.
igu e 7 11 Time elapsed until all jobs are processed 67
7.2.4 Implicit Enumeration Heuristic Results
The total travel distance for a given straddle carrier fleet size and various planning periods is shown in Figure 7.12. The figure shows that the optimal solution is achieved with six straddle carriers and a planning period of three minutes. The average wait time is 13:43 minutes. The time required to process all trucks is 10:13:40 hours (Appendix A, Table A.16).
The assignment with eight straddle carriers in operation and a 8-minute planning period has
0.2% longer travel distance compared to the best solution achieved. The average truck waiting time is reduced by 19.23%.
Figure 7.12 Total distance travelled as a function of the straddle carrier fleet size and the planning period
e seco es so u io as eig s a e ca ie s i o e a io a a a i g e io o eig mi u es E e i is esu is use as a ossi e so u io om e g a we ca o se e a e assig me wi a se e s a e ca ie s a wi e ou -mi u e a i g e io i e s o y y
93 mi es w ic is 9 mo e u e uces e a e age uck wai i g ime y 1 is so u io is e e e y o e e mi a o e a o a e ucke s ecause i yie s owe cos ( y
e uci g e equi me oo y o e s a e ca ie (1 5 a e ucks a e wai i g ess o se ice
A o ima so u io wi ess equi me i o e a io e a es e mi a o e a o o assig e cess equi me o o e a s o e e mi a (i e s o a i g o i o o o emo e (a se
e equi me i e o g u a e uce e ca i a cos
igu e 7 13 s ows e a e age wai i g ime o i e e s a e ca ie ee si e a
a i g e io
igu e 7 13 A e age uck wai i g ime o se ice 69
When the number of straddle carriers in operation is more than four, Figure 7.14 shows that the time required to process all trucks is similar, Only when four straddle carriers are in operation and the planning period is either 3 or 10 minutes, there is a significant difference in the time required to process all trucks.
Figure 7.14 Time elapsed until all trucks are processed
7.3 Comparison Between Heuristics
The optimal solution for each heuristic is shown in Table 7.2. The best solution in terms of the total distance travelled by straddle carriers is obtained with Hungarian I. The solution uses five straddle carriers and a seven-minute planning period. The solution results in 25%, 35% and 12.5% equipment reduction when compared to the FCFS, Hungarian II and the Implicit Enumeration Heuristic respectively. 7
e o a is a ce a e e is e uce y w e com a e o e C S so u io e a e age
uck wai i g ime i c ease su s a ia y w e com a e o o e eu is ics
a e 7 O ima So u io om ou eu is ics Im ici E ume a io C S u ga ia I u ga ia II eu is ic S a e Ca ie ee Si e 7 5 a i g e io (mi - 7 9 3 A e age Wai i g ime e 1 1 17 7 5 1 uck ( mm ss 13 3 A e age se ice ime o ucks 3 1 19 ( mm ss 7 15 15 51 A e age se ice ime o o - 9 3 9 5 o ucks ( mm ss 1 5 A e age se ice ime o ick- 3 1 3 7 31 u ucks ( mm ss 1 13 ime ee e o ocess a o s ( mm ss 1 11 17 11 3 1 1 5 1 13
o a is a ce a e e y 5 7 95 71 5 7 S a e Ca ie s (mi es
u ga ia II ma age o e uce e wai i g ime sig i ica y o 5 mi u es a 1 seco
A so i s ea o a i g i e s a e ca ie s i o e a io o ac ie e e o ima so u io eig
s a e ca ie s a e ee e o e i o e a io o mi imi e e o a is a ce a e e Com a e o
e C S u ga ia II e uce e o a is a ce a e e y 19 73 is is a 1 i c ease
com a e o u ga ia I
e Im ici E ume a io eu is ic i c ease e o a is a ce a e e y com a e
o u ga ia I a ec ease i y 39 a 3 9 com a e o u ga ia II a C S
es ec i e y e eu is ic as i c eases e o a is a ce a e e y 1 7 9 w e com a e o
u ga ia I e a e age uck wai i g ime is 13 mi u es a 3 seco s w ic is acce a e o a
a ge co ai e e mi a e a e age uck wai i g ime o se ice i c ease com a e o e C S
a u ga ia II A so e o ima o e a io usi g Im ici E ume a io eu is ic was ac ie e 71 wi a e uc io o 5 i s a e ca ie ee si e com a e o e u ga ia I a y 1 5 com a e o e C S Si ce is a go i m mi imi es e is a ce a e e y s a e ca ie s
u i g eac assig me e io i y is gi e o s a e ca ie s o ucks e a go i m o y io i i es e ucks i ey a e e cee e e ma imum a owe wai i g ime co s ai
e esu s s ow a y i o uci g a a i g e io a us aki g a a ages o k ow
uck-co ai e ai s wi o i e a e e a oca io o s a e ca ie s o ucks I is c ea a a
ee si e o 5 s a e ca ie s a a 7-mi u e a i g e io gi es e es esu s i e ms o mi imi i g e o a is a ce a e e y s a e ca ie s u si ce e a u e o e a go i m i
o o ce s a e ca ie s o ocess ucks a a e wai i g o a o g e io o ime e a e age wai i g ime i o acce a e o a ucke us e Im ici E ume a io eu is ic o i es a acce a e so u io i e ms o wai i g ime o ucks e ecomme a io is o use s a e ca ie s a a 3- mi u e a i g e io a us o i e a sig i ica y e e se ice o ucks wi a mi ima i c ease i o a a e is a ce y CHAPTER 8
SENSITIVITY OF THE STRADDLE CARRIER FLEET SIZE AND THE PLANNING
PERIOD DURATION WITH RESPECT TO THE TRUCK ARRIVAL RATE
is c a e ese s a a a ysis o ow e s a e ca ie ee si e a e u a io o e a i g
e io a e im ac e y i e e uck a i a a es o a s eci ic a i a a e e a a ysis e e mi es
e o ima s a e ca ie ee si e a ee s o e e oye a e o ima a i g e io e ge e a io o sam e a a is e ai e e o owe y e iscussio o esu s
8.1 Generating the Sample Data
o e e mi e e e a io s i e wee e uck a i a a e e s a e ca ie ee si e a e
u a io o e a i g e io a se ies o e e ime s we e co uc e I eac e e ime e s a e ca ie ee si e a e u a io o e a i g e io we e ca cu a e o a a icu a uck a i a a e uck a i a a e s o s is e ese e y e oisso ocess e i e -a i a imes
e wee e e s i e oisso ocess a e esc i e y e e o e ia is i u io
e Mo e Ca o ec ique was use o sam e a om a ia es Mo e Ca o sam i g assumes a " a om um e ge e a o " w ic ge e a es u i o m s a is ica y i e e e a ues o e a o e i e a [ 1 ( a ic e a a G ou
e uck i e -a i a ime is o ai e om e i e se u c io o e cumu a i e
is i u io u c io o e E o e ia is i u io ( w e e is e uck i e -a i a ime a is uck a i a a e
3 I oisso ocess e e s occu co i uous y a i e e e y o o e a o e
72 73
e me o o ogy is ese e e ow
(t is a a om a ia e w ic wi occu wi u i o m o a i i y e si y o [ 1] e
uck i e -a i a ime ca e e i e om e i e se u c io o
Mic oso E ce s a om um e ge e a o is use o ge e a e e a om uck a i a ime ase o equa io ( 1
e uck a i a a e ( um e o ucks a i i g u i g o e ou e s a e ca ie ee si e a e e g o e a i g e io i eac simu a io a e ese e i a e 1 o e age
o e am e o a uck a i a a e o 5 ucks/ ou e s a e ca ie ee si e was a ie om 3
o w i e e a i g e io was a ie om 1 o 1 mi u es us e i s sce a io o e uck a i a a e o 5 ucks/ ou a 3 s a e ca ie s i o e a io w i e e a i g e io was 1 mi u e e seco sce a io a e same uck a i a a e a s a e ca ie ee si e u a a
a i g e io o mi u es e ocess co i ues u i a com i a io s a e simu a e e e was a o a o sce a ios o simu a e ucks we e a i i g u i g a 1 ou e io wi 9 o
ucks a i e o ick-u co ai e s om e e mi a w i e 1 e i e e co ai e s o e
e mi a e uck s o oca io a e co ai e a is associa e wi a a icu a uck we e a so a om y ge e a e e u ga ia A go i m I was use o so e e assig me o em 4
bl 8. Simu a io Sce a io S uc u e
uck A i a a e 5 5 75 1 1 5 ( ucks/ ou S a e Ca ie om 3 o om 5 o 1 om o 1 om o 1 om 1 o 1 ee Si e om 1 o om 1 o 1 om 1 o 1 om 1 o 1 om 1 o 1 a i g e io 1 mi u es mi u es mi u es mi u es mi u es
um e o i e e 1 1 1 1 1 ema se s o a um e o ( -3 1 1 (1 -5 1 1 (1 - 1 1 (1 - 1 1 simu a io s o (1 -1 1 1 = s eci ic a i a a e = 500 = 5 = =
o a um e o 5 +5 + + + = simu a io s
8.2 C t f Str ddl C rr r Op r t n
o a s eci ic uck a i a a e e o ima s a e ca ie ee si e a e o ima a i g e io is e e mi e ase o e o a ai y cos o o e a io e cos associa e wi ocessi g ucks a e e e o e 1 ou s o o e a io is ig e ecause i is co si e e o e o e ime
e o a ai y cos o o e a io (i $ e ay co sis s o e s a e ca ie ow e s i a o e a i g cos s e S a e Ca ie ow e s i is e esse y equa io (
W e e
- u c ase ice (i $s
C — Ca i a eco e y ac o a is e esse y equa io 3
— Use u i e (i yea s 75
W e e
i - I e es ( iscou a e
e s a e ca ie u c ase ice is assume o e $ 1 e e ec e se ice i e is
15 yea s ( o o acoma a e e is e o sa age a ue a e e o e se ice i e e
Ca i a eco e y ac o (C w ic co e s a ese a ue i o a s eam o equa a ua
ayme s o e 15 yea s a a 5 55 i e es ( iscou a e is
e cos o ow i g s a e is e $99 e yea Assumi g a e e mi a o e a es
ays e yea a ig o i g e ai y com ou i g o i e es e ai y ow e s i cos is
$383.18 e s a e
e a ia e cos co sis s o o e a o wages s a e ca ie mai e a ce a wea a ea a e cos o ue a e ec ici y e a ia e cos is e esse y equa io (
W e e
Cs - O e a o wage
Cop — Mai e a ce a wea a ea
Ce - E e gy cos ( ue e ec ici y e c
e o e a o wage is ase o a a ua sa a y o $ 1 e yea wo k ays e yea e wage a e is $ 3 e ou u i g egu a ou s o o e a io a 57 9 $/ o o e ime
U io u es ic a e a e e mus e ee i e s o e e y wo s a es us e wage o io o a s a e s o e a i g cos is mu i ie y 1 5
e mai e a ce o e s a e ca ie u i g o e a io a wea a ea o i es ca es e c is assume o e 1 $/mi e 6
The straddle carrier cost of fuel and electricity is 4,2 $/mile. The total cost of energy is calculated as a total number of miles travelled by a straddle carrier during the day multiplied by the energy cost of 4.2 $/mile.
8. S l t n An l
The optimal straddle carrier fleet size and the planning period resulting from the simulation run are given in Table 8.2, For each specific arrival rate there are ten different simulation outputs based on ten different scenarios. The results are averaged across the scenarios, For example, the least cost operation for a truck arrival rate of 25 trucks/hour involves on average three straddle carriers and a five-minute planning period, yielding an average cost of $8,754.79 per day. The remaining optimal fleet size and planning period, the cost and selected performance measures for the arrival rates ranging from 25 truck/hour to 125 trucks/hour are shown in the table. Detailed results are given in
Appendix 8.4.
bl 8.2 Optimal Planning Period, Straddle Carrier Fleet Size and Performance for Different Arrival Rates
Arrival Rate (trucks/hour) 25 50 75 100 125
Straddle Carrier Fleet Size 3 6 8 10 13 Planning Period (min) 5 4 2 2 2 Average truck waiting time for 00:06:03 00:03:10 00:02:11 service (hh:mm:ss) 00:01:47 0:01:13 Average service time per truck 00:09:47 00:06:40 (hh:mm:ss) 00:05:50 00:05:20 0:04:38 Average service time for drop-off 00:04:57 00:03:16 truck (hh:mm:ss) 00:02:09 00:02:01 0:01:40 Average service time for pick-up 00:10:25 00:07:04 truck (hh:mm:ss) 00:06:14 00:05:43 0:04:57 Time Needed To finish all jobs 10:04:07 10:07:31 (hh:mm:ss) 10:09:21 10:05:12 10:05:47 Total Distance Travelled by 386.56 764.90 1168.53 1511.01 Straddle Carriers (miles) 1824,26 Total Cost of Operation Per Day 8,754.79 16,722.85 24,381.66 ($/day) 31,132.17 38,494.37 77
By doubling the arrival rate from 25 to 50 trucks per hour, the optimal straddle carrier fleet size also doubled (from 3 to 6) and the planning period was reduced from 5 to 4 minutes. As the arrival rate increased, in general, as shown in Table 8,2 and Figure 8.1, the number of straddle carriers in operation also increased. To accommodate a higher number of requests for service, the duration of the planning period was reduced.
Figure 8.1 Optimal planning period and straddle carrier fleet size for different truck arrival rates
8.4 Impact of Load Imbalance
To evaluate the impact of split between import and export containers arriving at the terminal, an experiment was conducted. In this experiment, the truck arrival rate was 100 trucks per hour, and the percentage of import containers varied between 10 and 80%. Figure 8.2 shows that the minimum cost is achieved when the operation is perfectly balanced. In this operation, 50% of trucks are coming to pick up import containers and 50% are arriving to deliver export containers. 7
igu e o a ai y cos as a u c io o e ce age o im o ucks a i i g a e e mi a
owe e e s a e ca ie ee si e a a i g e io a e o se si i e o e oa im a a ce ( is i u io o im o a e o ucks as s ow i a e 3 e s a e ca ie ee si e emai s i e a 1 s a es a e a i g e io is mi u es
a e 3 O ima S a e Ca ie ee Si e a a i g e io as a u c io o Im o ucks A i i g a e e mi a
Optimal Assignment Distribution Straddle Carriers Fleet Size Planning Total Daily Cost Period 90% imports 10 2 $ 31,132.17 10% exports 80% imports 10 2 $ 29,753.39 20% exports 50% imports 10 2 $ 28,317.77 50% exports 30% imports 10 2 $ 29,367.94 70% exports 20% imports 10 2 $ 30,751.92 80% exports
8. E n f S l n Str ddl Op r t n
a e s ows a c a ge i e o ima cos o o e a io a e s a e ca ie ee si e
ee e w e e a i a a e c a ges e e a e eco omies o sca e ese i e ms o cos s As e a i a a e i c eases y 1 ( o 5 ucks/ e cos o o e a io is i c ease o y y 91 I e a i a a e is u e i c ease o 75 ucks/ ( i c ease com a e o e i i ia case e o a cos is i c ease y 17 is e is o se e i e s a e ca ie ee si e as we W e e a i a a e is ou e e o a um e o s a es a so ou e a w e e a i a a e i es
e s a e ca ie ee si e i c eases y 1 7
a e C a ge
A i a a e ( ucks/ ou 5 5 75 1 1 5
I c ease i uck a i a a e ( 1 3
I c ease i cos o o e a io ( 91 17 1 3
I c ease i equi me ( 1 1 7 33 333
I e cos o o e a io is e esse usi g e a e age cos e co ai e e esu s a e s ow i a e 5 a igu e 3 is esu s ow a e a e age cos s a e ec easi g is mea s
a e ig e e um e o eques o se ice e owe e e e o i es me o eac uck
ocesse
bl 8. A e age Cos e Co ai e
A i a a e ( ucks/ ou 5 5 75 1 1 5
A e age Cos e Co ai e ($/co ai e $ 35 $ 33 5 $ 3 51 $ 31 13 $ 3 80
Figure 8.3 Average Cost per Container
8.6 Implications of Sampling Deviation
The solutions of the straddle carrier fleet size and planning period optimization are determined for a simulated random sample of arrival times, container locations, and truck slot locations. Each solution represents the mean of the total daily costs for a series of simulations (10 simulations in this analysis). To compare the solutions and determine the optimal solution for the problem, it is necessary to conduct a statistical analysis of results comparing the means and standard deviations of the total cost of operations for all analyzed combinations of straddle carrier fleet sizes and planning periods. Consideration of standard deviations is important as differences between means may be larger than corresponding standard deviations, making it difficult to conclude which solution is the optimal one.
As an example, for the operation with 13 straddle carriers, a planning period of 2 minutes, and an arrival rate of 125 trucks/hour, the sample mean of the total cost of operation is 38,494.37
$/day and the standard deviation is 999.05 $/day, For an operation with 12 straddle carriers, a 1 planning period of 1 minute, and arrival rate of 125 trucks/hour, the sample mean of the total cost of operation is 38,997.45 $/day and the standard deviation is 1,996.37 $/day ( igu e 9 3 e i e e ce between the means is 503.08 $/day, which is less than one standard deviation of either solution. This analysis implies that the standard deviations of the means in the simulation experiments will have to be reduced in order to make a conclusive determination of the optimal solution. This can be achieved by increasing the sample size, i.e. performing a larger number of simulations for any given truck arrival rate, straddle carrier fleet size, a planning period. Pair-wise statistical tests of the difference between the means will have to be conducted to compare the solutions for any given truck arrival rate to determine the optimal solution with a satisfactory confidence level.
igu e Sample means and one standard deviation range
7 Co c usio s e esu s o e e e ime ese e i is c a e yie e o owi g co c usio s
- e o ima s a e ca ie ee si e a e a i g e io e e o e uck a i a a e
- o a s eci ic a i a a e e e mi a o e a o ca use e a go i ms e e o e i is
isse a io o e e mi e e o ima s a e ca ie ee si e a e o ima e g o e
a i g e io a mi imi e e o a ai y cos o o e a io
- e s a e ca ie ee si e a a i g e io a yie a o ima so u io is o
se si i e o e a ic im a a ce( e ce age o im o a e o co ai e s a i i g u i g e ay
- e e seem o e is eco omies o sca e i s a e ca ie o e a io s A o o io a e
i c ease i e uck a i a a e esu s i a sma e i c ease i e o a cos a e s a e
ca ie ee si e ese a e ike y eco omies o sca e ue o a ic e si y 8
C A E
MA AGEME IM ICA IO S
e a e-o e wee e cos o o e a io a se ice qua i y o i e o ucks is ese e i is c a e C a e ou a o a gi e a i a a e e e is a o ima so u io i e ms o e s a e ca ie ee si e a e a i g e io a yie e owes cos o o e a io e i i gs
om C a e a e use i a case s u y o i us a e e a ica io o e ese e mo e o a sam e ai y o e a io o e o e mi a e ici g s uc u e o a uck a oi me sys em is
iscusse I is sys em ucke s make a a geme s o a emium se ice y sc e u i g i a a ce
e a i a s a e e mi a a a ce ai ime e o e a o c a ges a emium a e o suc se ice
. r d ff t n th C t f Op r t n nd S rv Q l t
e esu s o e e e ime s i C a e ca e use y o e mi a ma ageme o e ami e e im ac o s a e ca ie ee si e a e a i g e io o e cos o o e a io a se ice qua i y Ma ageme ca e e o a equi me e oyme a a o i es e e se ice o ucks ( y mi imi i g ei wai i g ime o mi imi es e o a cos o o e a io
As a i us a io e o ima a e seco es so u io s a e a a y e o e o e a io wi a uck a i a a e o 5 ucks/ ou e o ima o e a io i o es a s a e ca ie ee o s a e ca ie s a a -mi u e a i g e io ( igu e 9 1 e seco es so u io wi i e e
ee si e i o es 5 s a e ca ie s a -mi u e a i g e io e cos i e e ce is $1 3
e ay o a o ima e y 1 1 o e o a ai y cos o o e a io wi s a e ca ie s e a e age uck wai i g ime wi 5 s a e ca ie s is mi u e a seco s as com a e o 3 mi u es a 1 seco s wi s a e ca ie s a i e e ce o 1 mi u e a 1 seco s o 1
us e e mi a o e a o is i cu i g a ma gi a i c ease i cos y a i g o e a i io a s a e ca ie a ai a e 84
Figure 9.1 Total daily cost of operation for different straddle carrier fleet and truck arrival rate of 50 trucks/hour
Alternatively, the terminal can reduce the planning period from 4 to 2 minutes, while keeping the fleet at 6 straddle carriers. This results in a reduction of the average truck waiting time to 1 minute and 30 seconds, a 57.13% decrease (Appendix B.1 Table B.1.3), The total cost of operation increased marginally by $51.70 per day or 0.31%.
The optimal solution for a truck arrival rate of 125 trucks/hour involves 13 straddle carriers and a 2 minute planning period (shown on Figure 9.2). The reduction of the fleet to 12 straddle carriers and reduction of a planning period to 1 minute, results in reducing the average truck waiting time from 1 minute and 13 seconds to 55 seconds, while increasing the total cost by $500.08 per day.
Thus, it is possible to improve service for trucks by incurring a minimal increase in cost of 1.31%. 5
igu e 9 Total daily cost of operation for different straddle carrier fleet and truck arrival rate of 125 trucks/hour
The terminal operator can reduce average truck waiting time even further by reducing the planning period to one minute, while keeping the fleet size to 13 straddle carriers. The average uck waiting time is reduced to 36 seconds, and the cost of operation increased by $65.65 per day or 0.17% compared to the optimal solution.
9 a i g S a e Ca ie O e a io s wi a ia e uck A i a a es
oug ou e ay
Based on the results of the analysis presented in Chapter 8, the terminal operator can resourcefully develop an optimal operating plan that will utilize equipment more efficiently. The graph shown in
Figure 9.3 represents a sample distribution of truck arrivals at the port terminal during a 10-hour work day. The entire work day can be divided into time intervals including the hours with similar truck arrival rates. For each interval (and thus the corresponding truck arrival rate), the optimal fleet of s a e ca ie s a e o ima a i g e io ca e o ai e usi g e esu s ese e i
C a e
igu e 9 3 is i u io o uck a i a s i a o e mi a y ou o e ay
e us i s assume a e e mi a o e a o i is case s u y o e a es a i e s a e ca ie ee oug ou e ay e o ima o e a i g a yie i g e mi imum o a ai y cos o o e a io i o es 1 s a e ca ie s a a a i g e io o 3 mi u es e o a ai y cos o is o e a io is $1 5 a e a e age uck wai i g ime is 1 mi u e a 3 seco s e e ai e
esu s o e a a ysis a e s ow i a e 9 1 a i A e i A ( a e A 7
a e 9 1 Cos o O e a io a e o ma ce o 1 S a e Ca ie s A e age Wai i g ime e uck ( mm ss 1 3
A e age Se ice ime e uck ( mm ss 3 5
A e age Se ice ime e o -O uck ( mm ss 1 9
A e age Se ice ime o ick-U uck ( mm ss
Time needed to process all jobs (hh:mm:ss) 10:15:45
Total Distance Travelled by Straddle Carriers (miles) 481.2977
o a Cos ($/ ay 1 5 7
o im o e e ai y e mi a o e a io s e e i e wo k ay ca e i i e i o i e
i e e ime i e a s e ume a e I- as s ow i igu e 9 o eac i e a a a e age uck a i a a e is ca cu a e ase o e a e age uck a i a a e a e a a ysis ese e i C a e
a o e a io a a is se ec e o eac i e a i c u i g e s a e ca ie ee si e a
a i g e io ( a e 9
igu e 9 ime i e a s ase o simi a uck a i a a es
a e 9 e mi a O e a i g a s o i e e ime I e a s u i g e Wo k ay
Operational Plan ED I II III IV V
Hour ID 1 2 3 4 5 6 7 8 9 10
Actual hourly arrival rates (trucks/hr) 44 53 48 55 100 75 45 55 50 25
Average Arrival Rate (trucks/hr) 50 100 75 50 25
Straddle Carrier Fleet Size 6 10 8 6 3
Planning Period (min) 4 2 2 4 5 88
ime I e a I w ic i c u es e i s ou o e a i g ou s as a a e age uck a i a a e o 5 ucks/ ou ase o e a a ysis i C a e a usi g e o ima so u io o a a i a
a e o 5 ucks e ou e o ima o e a io a a o is i e a (O e a io a a I i o es
s a e ca ie s a a a i g e io o mi u es I e same ma e e o ima s a e ca ie ee si e a e a i g e io a e e e mi e o o e a io a a s II oug ( a e
9
e us i s assume a e e mi a o e a o as a ee o 1 s a e ca ie s a ai a e a a
imes u o e a es ase o e o e a io a a s ese e i a e 9 is mea s a some s a e ca ie s wi e i e i ce ai i e a s o e ay (e g s a e ca ie s wi e i e i
I e a I w i e s a e ca ie s wi e i o e a io e o a cos o o e a io i is case is
1 559 1 $/ ay ( a e 9 3 is is a e uc io i e o a ai y cos o o e a io s as com a e
o e e ious case w e e a 1 s a e ca ie s wo k a ay
bl . Cos o O e a io a e o ma ce wi I e a - ase O e a i g a s a i e S a e Ca ie ee
A e age Wai i g ime e uck ( mm ss
A e age Se ice ime e uck ( mm ss 17
A e age Se ice ime e o -O uck 3 ( mm ss A e age Se ice ime o ick-U uck ( mm ss 3
ime ee e o ocess a o s ( mm ss 1 11 3
o a is a ce a e e y S a e Ca ie s (mi es 7 3
o a Cos ($/ ay 1 559 1
owe e a e mi a o e a o ca a e a o e a io a a ows e equi me i c u i g s a e ca ie s o e s a e e wee i e e ya s o o e a i g su -sys ems wi i e e mi a I 8
is case e i e s a e ca ie s u i g ime i e a s I III, I , a cou e e oye o o e
asks wi i e same e mi a (e g s i - o-s o e o e a io s ai ya o e a io s e c is wou i e ec e uce e s a e ca ie ow e s i a associa e a o cos o e a a y e ya o e a io as ese cos s wou ow esi e wi e o e a io wi i e e mi a a u i i es e equi me a wou o e wise e i e o e am e e ou s a e ca ie s a we e i e u i g ime I e a I i e e ious sce a io wou e e oye o e ai ya e ow e s i a a o cos o ese s a e ca ie s u i g ime I e a I wou e c a ge o ai ya o e a io s e uci g e cos o
e a a y e uck o e a io s I is sce a io is im eme e i e a a y e e mi a e o a cos o o e a io s ecomes 1 9 1 1 $/ ay ( a e 9
bl .4 Cos o O e a io wi I e a - ase O e a i g a s a S a e S a e Ca ie ee
A e age Wai i g ime e uck ( mm ss
A e age Se ice ime e uck ( mm ss 17 A e age Se ice ime e o -O uck ( mm ss 3 A e age Se ice ime o ick-U uck ( mm ss 3 ime ee e o ocess a o s ( mm ss 1 11 3
o a is a ce a e e y S a e Ca ie s (mi es 7 3
o a Cos ($/ ay $1 9 1 1
e ai y cos o o e a io wi a i e s a e ca ie ee is $3 7 1 ig e a e
ai y cos o o e a io wi a s a e s a e ca ie ee a 9 i e e ce e isa a age o
is o e a io is a s a e ca ie s a e imi e o ocess o y ucks w i e i e o e a io wi a s a e s a e ca ie ee s a e ca ie s ca e e oye o i e e asks wi i e e mi a
e uci g e i i g im o i g e equi me u i i a io a u ima e y e uci g e equi me ow e s i a associa e a o cos 0
. r n Str t r f n App nt nt S t
e o s o os A ge es a o g eac i Ca i o ia i o uce a og am ca e O eak i u y
3 wi e o ec i e o s i o e a e uck a ic om eak o o - eak ou s o e ay (i e o e e i g a ig ou s w e e e is ess co ges io o ea y ig ways e i ea e i e
og am was o e e usi ess ou s o e o e mi a s a i s i u e a eak a ic Mi iga io ee
( M w ic wou e i e ec u i g eak ou s us e cou agi g ucke s (a s i e s o u i i e o - eak ou s o e ma imum e e ossi e ( I /II C
e k ow e ge o o ima s a e ca ie ee si e a a i g e io o a gi e uck a i a a e o i es a goo ou a io o a a io a ici g o uck a oi me s A goo
ici g o icy wou a em o se ices so a ey co e e a e age o ma gi a cos o a i g a uck- ac o a i s co ai e e e o e i e e o i g a goo ici g o icy o a oi me s e
i s s e is o es ima e e cos o a i g a uck a as a a oi me
e cos o se ici g a uck wi a a oi me wi equa e ma gi a (o i c eme a cos e wee e o e a io wi a oi me s a e o e wi ou a oi me s I a oi me s
equi e a ew s a e o e oug i e e cos o is s a e mus e a oca e amo g se e a
ucks a a e a oi me s I o y o e uck is a e e e cos o e s a e s ou e
u y a oca e o is a icu a uck
e o a e a e age cos s o a i g a uck w e ucks a i e a e 5 ucks e ou
a e wou e $35 e uck I we sc e u e 5 mo e ucks o a i e u i g e same ou e a i io a cos wou e a o ima e y $31 7 5 e a oi me is cos is sma e com a e o a e age cos o $ 33 5 e co ai e o a 5 ucks/ ou uck a i a a e (C a e 5 a e 5
e e wi e o a i io a cos o e o e a o i e a oi me is ma e u i g a e io w e su icie s a es a e o e a i g o se e is a oi me
is is equa e o a cos o o e a io $ 75 79 i i e y e 5 ucks se e 5 is is equa e o a cos o o e a io o 5 ucks e ou o $1 7 5 mi us e cos o o e a io o 5 ucks o $ 75 79 i i e y ose 5 a i io a ucks se e e ay
As o e ici g e o e a o s ou c a ge e a e ( ice a co e s e ma gi a cos e wi i cu o se ice a a oi me I e ma gi a cos is e o e o e a o wi ea e cess o i
o a oi c a gi g a es a may uc ua e so as o e ec e ac ua ma gi a cos e o e a o may
o ecas e um e o a oi me s a wi occu (o agg essi e y ma ke e a oi me sc e u e a i e ec sc e u e em oge e so a i as a ce ai a ge o u a io o ucks a wi s a e i e cos o a s a e Suc ma ageme o a oi me s wi a oi a si ua io w e e a uck is c a ge a a e a wi co e 1 o e ma gi a cos o e ay a o y 1 i e uck is o e amo g a g ou o 1 a oi me s
I s ou e oi e ou a e ma gi a cos o a a oi me go sma e w e e
o ume i c ease ue o ese ec easi g eco omies o sca e e o e a o s a s o make e cess
o i e i s a o e e cess o i is ue o e ec easi g ma gi a cos s e seco a wi come om e c a gi g o a emium a oi me a e i e cess o e o igi a a e age cos o e am e i e o e a o we e o c a ge a a oi me a e o $ e co ai e e $3 15 wou come om e ec ease ma gi a cos a $ 9 wou come om e i e e ce e wee e a oi me ice a e o igi a a e age cos
.4 C n l n
is c a e o i e a i sig a o e e ac ica gui e i es o ow e e mi a o e a o ca ma age e s a e ca ie ee
y emo i g equi me om e o e a io e cos o o e a io i c ease y a sma
e ce age e e e i o gai i g a a i io a s a e ca ie o e em oye e sew e e i e
e mi a is im o a es ecia y i e e is a ee o i c ease e o uc i i y i o e e mi a ya a eas e uci g e a i g e io o e same s a e ca ie ee si e ea s o a sma e uck wai i g ime A so i is ossi e o e uce e a i g e io a e s a e ca ie ee si e a 2 im o e se ice o ucks y i cu i g a mi ima a i io a cos o e e o ima so u io (seco e am e C a e 9 1 e a i io a cos is i e 3 o 1 31 a ge
ecog i i g simi a uck a i a a e s o e mi a ma ageme ca e e o o e a io s
ase o ese uck a i a a e s e mi a ma ageme ca e e mi e o ima e s a e ca ie
ee si e o eac uck a i a a e a a e o e a io acco i g y is e a es ossi e
e oyme o e cess s a e ca ie s o i e e ya s C A E 0
CO C USIO A E ECOMME A IO S O U U E ESEA C
is isse a io e e o e se e a ew a i o a i e o imi a io mo e s o e o e a io a assig me o s a e ca ie s a e co ai e e mi a I a i io o e assig me e o ima s a e ca ie ee si e is ca cu a e U ike e s a ic si g e e io o imi a io w e e esou ces a e o ima y a oca e o ce a o y o ce i ea ime o imi a io i is c i ica o e e mi e a o ima e io u i g w ic e accumu a io o uck eques s o se ice wou occu e o e e s a es a e is a c e o se ice ese eques s is isse a io makes a co i u io o e ie o ea ime a s o a io equi me is a c i g y i s i o uci g e co ce o e a i g
e io a e ca cu a i g i I is c a e e esu s a i i gs a e summa i e a ecomme a io s o u u e esea c a e ese e
0. lt nd nd n
A com e e si e i e a u e e iew was co uc e o a ious im eme a io s o assig me a go i ms a sc e u i g use o assig co ai e a i g equi me i a co ai e e mi a e
i e a u e e iew i o i a y a em o e ayi g se ice o ucks o a s eci ic e io o ime i o e o e e e oy equi me e i e a u e e iew ese e gui e i es o a assig me a go i m im eme a io a ese e e o ma ce a ame e s a ca e use o e a ua e e e icie cy o e o e a io
C a e ese e e issues a e e mi a o e a o aces u i g e ai y o e a io e
o io o o ima i y i ea ime o imi a io was e ai e a e co c usio as o e o ima i y o
e assig me o s a e ca ie s o ucks we e ma e
94
e assig me mo e s ese e i C a e we e use i C a e o a ac ua ea wo
uck a i a a e o ai e om e e mi a o e a o i o ewa k ew e sey e mi a o e a o e assig me i w ic e co s a um e o s a e ca ie s is o e a i g oug ou e w o e ay was a a y e e i i gs a e summa i e e ow
- e u ga ia A go i m I o i e e es so u io i e ms o mi imi i g e o a
is a ce a e e y s a es u i o co si e e uck wai i g ime a e cessi e
wai i g ime a a e o acce a e o ucke s e o g wai ime makes is a oac
u a ac i e o ucke s a us i icu o im eme i ac ua o e a io
- e u ga ia A go i m II o i es acce a e uck wai i g imes u equi es e
a ges s a e ca ie ee si e o e em oye
- e eu is ic ase o Im ici E ume a io o i es a acce a e so u io o e
e cessi e wai i g ime o ucks e ecomme a io is o e oy s a e ca ie s a
a a i g e io o 3 mi u es Sig i ica y e e se ice is o i e o ucks wi a
mi ima i c ease i o a a e is a ce o
e im ac o e uck a i a a e o s a e ca ie ee si e a a i g o i o was e ami e i C a e e esu s o e e e ime ese e yie e o owi g co c usio s
- e o ima s a e ca ie ee si e a e a i g e io e e o e uck a i a a e
- o a s eci ic a i a a e e e mi a o e a o ca use e a go i ms e e o e i is
isse a io o e e mi e e o ima s a e ca ie ee si e a e o ima e g o e
a i g e io a mi imi e e o a ai y cos o o e a io
- e s a e ca ie ee si e a a i g e io a yie a o ima so u io is o
se si i e o a ic im a a ce( e ce age o im o a e o co ai e s a i i g u i g e
ay 95
- There seem to exist economies of scale in straddle carrier operations, A increase in the truck
arrival rate results in a smaller increase in total cost and the straddle carrier fleet size. These
are likely economies of scale due to traffic density.
The trade-offs between the cost of operation and providing better service to trucks were presented in Chapter 9, The results show that port terminal management can alter the operational parameters without much sacrifice in cost, The shallowness of the cost function enables the removal of the straddle carrier from operation with a negligible cost increase (1.12%, Chapter 9,1) and reduction of the truck waiting time. Also, by changing the duration of the planning period and operating with fixed straddle carrier fleet size, the truck waiting time is reduced and the cost of operation increases by 0.17%, The managerial implication is that the straddle carrier fleet and planning period can be adjusted to meet a specific goal, and total cost will have minor deviations from the optimal solution.
The operation with variable straddle carrier fleet size and planning period was presented in
Chapter 9.3. The entire work is divided into time intervals including the hours with similar truck arrival rates, For each interval (and thus the corresponding truck arrival rate), the optimal fleet of straddle carriers and the optimal planning period can be obtained using the results presented in
Chapter 8. The cost of operation, compared to the cost with fixed fleet size, decreased by 28.96 % if the straddle carriers be shared between different yards or operating sub-systems within the terminal, If unused straddle carriers are idle the cost of operation is reduced by 0,6 %,
The dissertation concludes with the discussion of the pricing structure of an appointment system, The knowledge of optimal straddle carrier fleet size and planning period provides a foundation for rational pricing of truck appointments. To develop a good pricing policy for appointments, the first step is to estimate the cost of handling a truck that has an appointment. 6
0.2 C ntr b t n f th r h
The methodology and findings of this dissertation would contribute in two areas (1) Assignment
Algorithms Application and (2) Container Terminal Operation Management,
0.2. C ntr b t n t A n nt Al r th Appl t n
This dissertation contributes to the application of assignment algorithms by implementing the concept of the planning period with the goal of developing an efficient allocation of straddle carriers to trucks.
The research reflects an effort to implement different assignment strategies to model an important problem which arises in container terminal operations. The relationship between the planning period and straddle carrier fleet size and how they affect the operational parameters was considered in the analysis. The assignment concept can be a part of a decision support system to aid planning for container terminal operations,
0.2.2 C ntr b t n t rt r n l M n nt nd Op r t n
The assignment concept presented in this dissertation can be a useful tool for port terminal management to evaluate different operational scenarios and can aid the decision-making process, The presented methodology and analysis provide management with tools to improve the operations. The results of the analysis presented in Chapter 8 provide decision makers insight in following areas:
- The analysis provides the optimal straddle carrier fleet size and planning period for specific
truck arrival rate that will minimize the cost of operation. The total cost of operation is
minimized while providing an acceptable level of service to trucks.
- The analysis evaluates the change in the total cost of operation, if the terminal operator would
like to improve the service to trucks. Guidelines are presented for the port terminal operator
to evaluate how the cost of operation will change, if the planning period and (or) straddle
carrier fleet size is changed,
- The analysis provides insights on pricing, if an appointment system is implemented.
0. nd t n f r t r r h
Different objective function can be investigated in the future, and their advantages and disadvantaged can be explored. The objective function used in this research is to minimize the total distance travelled. A weighted objective function of distance travelled and truck waiting time could be implemented, Different priorities can be given to distance travelled or waiting time by varying a and 13 presented in equation 10.3.1
Objective Function = straddle distance travelled + *delay in servicing trucks (10.3.1)
Where:
is a travel cost per unit distance for straddle carrier
13 is a linear cost penalty for delay in servicing a truck
When trucks enter the terminal, the terminal operator knows which trucks arrived to pick-up containers, and which trucks are delivering containers. The assignment strategy that will recognize these service request and pair drop-off jobs and pick-up jobs within the planning period can be a part of future research.
Optimizing the operation between two or more yards, with shared straddle carrier fleet, can be explored as a part of future research, The optimal operational plan can be determined that provides an effective straddle carrier utilization, while ensuring satisfactory level of service to customers.
When analyzing the impact of truck arrival rates on straddle carrier fleet size and planning period, the optimization of the truck slot positions and container positions in the yard should be considered as well,
An appointment system can be implemented and its impact on the cost of operation should be determined. By redistributing the portion of trucks arriving during the peak hour to the non-peak period, a possibility to use smaller straddle carrier fleet to process service requests should be investigated, The pricing of services and incentives for truckers to make appointments should be 8 i es iga e e ice o ow muc e e mi a o e a o s ou c a ge o a a oi me o a ac
ucke s o u i i e e o - eak ou s s ou e i es iga e
e o ewa k Co ai e e mi a e e ie ce is a o e i g ucke s e o io o a i e
u i g o - eak ou s a a e muc s o e u -a ou ime i o a ac ucke s o is se ice
A s u y ca e co uc e i e u u e o i es iga e e e e o se ice ( uck se ice ime a wi a ac a ig e e ce age o ucks o u i i e e o - eak ou se ice A E I A
ESU S O S A E CA IE S ASSIG ME S
A. n r n Al r th I t n r W t
bl A. ee S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
3 S a e Ca ie s i O e a io
a i g e io (mi
1 3 5 7 9 1
A e age Wai i g ime e uck 7 31 1 53 3 9 1 9 1 35 3 9 ( mm ss 5 53 59 3 1 9 3
A e age Se ice ime e uck 3 9 1 7 1 1 3 1 37 3 11 3 1 ( mm ss 3 1 3 15 5 3 7 5
A e age se ice ime e o - O uck 11 1 7 3 3 5 1 5 35 1 7 1 1 33 1 1 3 3 3 37 3 5 9 ( mm ss A e age Se ice ime o ick-U uck 3 7 53 9 55 1 1 3 3 37 3 1 3 3 3 7 3 39 5 5 ( mm ss ime ee e o ocess a o s 1 3 5 1 5 1 57 39 11 51 1 3 ( mm ss 13 5 9 1 55 5 15 3 7 15 5 57 15 5 1
o a is a ce a e e y s a e Ca ie s 55 5 5 33 93 11 95 5 5 7 9 5 3 51 5 5 (mi es 531 11 5 9 11
1
a e A ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
1 3 5 7 9 1
A e age Wai i g ime e uck 5 5 13 1 31 59 1 3 1 9 13 1 9 17 7 3 ( mm ss A e age se ice ime e uck 3 15 39 3 51 33 1 1 1 5 1 5 3 1 51 5 15 1 31 19 9 39 ( mm ss A e age se ice ime e o - o uck 7 1 13 3 9 1 51 3 5 1 1 31 1 5 ( mm ss A e age Se ice ime o ick- U uck 9 19 1 5 1 37 1 1 3 1 3 51 1 5 3 3 59 3 5 ( mm ss ime ee e o ocess a o s 1 13 3 1 13 3 1 13 1 5 11 5 11 53 35 1 3 13 3 9 13 39 55 13 59 1 ( mm ss o a is a ce a e e y s a e Ca ie s 55 3 5 1 5 (mi es 5 97 15 9 3 93 551 5 5 5 3
a e A 3 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
5 S a e Ca ie s i O e a io
a i g e io (mi
1 3 5 7 9 1
A e age Wai i g ime e uck 1 5 3 9 1 1 7 3 5 7 1 17 7 1 31 3 1 7 17 ( mm ss A e age Se ice ime e uck 5 13 3 1 3 9 5 55 1 19 1 33 1 9 1 1 ( mm ss A e age se ice ime e o - 1 5 3 15 51 O uck 33 3 3 9 5 3 3 ( mm ss A e age Se ice ime o ick- U uck 1 3 9 1 1 3 3 1 1 1 3 1 51 13 5 11 ( mm ss ime ee e o ocess a o s 1 13 1 13 1 13 1 1 3 1 1 51 1 5 3 11 3 11 7 5 1 15 1 39 5 ( mm ss o a is a ce a e e y S a e Ca ie s 53 9 5 5 3 5 7 9 1 9 17 7 5 9 79 5 51 9 515 5 (mi es 0
bl A.4 Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
1 3 5 7 9 1
A e age Wai i g ime e uck 5 1 3 17 57 1 3 9 5 51 57 1 9 5 1 5 1 35 5 ( mm ss A e age se ice ime e uck 3 1 5 39 7 19 1 1 3 5 1 11 9 1 5 1 37 59 ( mm ss A e age se ice ime e o - o uck 1 19 1 5 3 3 1 5 3 39 57 5 ( mm ss A e age se ice ime o ick- 3 31 5 7 5 15 19 33 1 U uck 5 5 1 31 1 3 1 1 53 5 ( mm ss ime ee e o ocess a o s 1 11 1 1 1 13 1 1 3 1 1 1 1 51 1 3 31 11 9 13 11 1 59 11 37 ( mm ss o a is a ce a e e y S a e Ca ie s 511 1 3 5 5 5 1 97 7 1 5 59 1 1 (mi es 7 1 5
bl A. Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
7 S a e Ca ie s i O e a io
a i g e io (mi
1 3 5 7 9 1
A e age Wai i g ime e uck 3 1 1 1 57 5 3 13 9 3 1 3 1 1 3 ( mm ss A e age Se ice ime e uck 3 3 3 1 5 15 15 55 31 5 5 3 1 1 ( mm ss A e age se ice ime e o - O uck 1 7 1 3 9 51 19 9 3 3 3 19 55 19 ( mm ss A e age se ice ime o ick- U uck 3 1 3 55 3 5 33 9 1 17 9 3 35 5 57 1 15 1 3 3 ( mm ss ime ee e o ocess a o s 1 11 1 1 1 13 1 1 3 1 1 1 1 9 1 17 1 3 17 1 5 11 31 ( mm ss o a is a ce a e e y S a e Ca ie s (mi es 5 3 9 5 3 5 5 17 3 5 7 9 19 5 7 102
Table A.6 Eight Straddle Carriers in Operation with a Planning Horizon of Varying Duration
8 Straddle Carriers in Operation
Planning Period (min)
1 3 5 7 9 1
A e age Wai i g ime e uck 35 1 1 39 19 3 9 3 1 3 9 1 ( mm ss A e age se ice ime e uck 55 3 5 3 55 37 5 7 1 39 1 31 3 5 1 ( mm ss A e age Se ice ime e o - o uck 1 1 5 1 5 3 3 1 3 5 7 35 9 5 5 ( mm ss A e age Se ice ime o ick- 3 9 3 1 5 11 3 17 9 U uck 3 1 5 37 1 11 3 ( mm ss ime ee e o ocess a o s 1 11 1 1 1 1 13 1 1 5 1 1 1 1 9 1 17 1 1 51 1 3 1 1 31 ( mm ss o a is a ce a e e y 9 351 9 7 975 1 57 597 7 7 59 3 7 53 15 S a e Ca ie s (mi es
Table A.7 Ten Straddle Carriers in Operation with a Planning Horizon of Varying Duration
10 Straddle Carriers in Operation
Planning Period (min)
3 5 7 9 1 A e age Wai i g ime 35 1 1 3 7 39 3 3 39 11 1 1 3 3 e uck A e age Se ice ime e uck 9 3 1 3 5 51 5 13 1 53 3 3 ( mm ss A e age Se ice ime e o - 5 1 1 1 9 7 3 3 13 3 5 57 1 37 o uck ( mm ss A e age se ice ime o ick- 3 3 3 3 5 5 9 19 1 9 9 3 U uck ( mm ss ime ee e o ocess a o s 1 11 1 1 1 15 5 1 1 5 1 17 1 17 9 1 17 1 1 5 1 17 5 1 1 ( mm ss o a is a ce a e e y 13 1 9 7 37 71 7 5 9 9 3 91 7 575 1 5 s a e Ca ie s (mi es 0
A.2 n r n Al r th II W th r r r t
bl A.8 ee S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io 3 S a e Ca ie s i O e a io a i g e io (mi 5 7 9 1 A e age Wai i g ime e uck ( mm ss 1 35 5 17 3 9 9 1 5 55 A e age Se ice ime e uck ( mm ss 1 9 1 5 5 3 5 3 9 5 3 A e age Se ice ime e o -O uck ( mm ss 3 5 1 3 5 7 1 1 13 A e age Se ice ime o ick-U uck ( mm ss 1 35 5 59 11 9 3 3 35 1 31 1 3 19 ime ee e o ocess a o s ( mm ss 1 5 1 11 9 11 1 1 1 39 1 3 3 1 3 3 o a is a ce a e e y S a e Ca ie s (mi es 55 519 9 5 1 1 51 519 9 517 3 5 3
bl A. ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age Wai i g ime e uck ( mm ss 3 5 1 13 51 51 1 1 3 9 5 A e age Se ice ime e uck ( mm ss 5 1 1 1 11 11 1 1 1 1 A e age Se ice ime e o -O uck ( mm ss 53 3 53 5 3 11 5 51 5 A e age Se ice ime o ick-U uck ( mm ss 5 5 5 17 53 11 55 13 35 13 3 1 51 ime ee e o ocess a o s ( mm ss 11 1 3 3 1 1 51 1 17 1 1 5 1 1 3 1 1 o a is a ce a e e y S a e Ca ie s (mi es 517 51 3 51 9 5 5 7 9 3 93 104
Table A.10 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
5 S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age Wai i g ime e uck ( mm ss 3 5 7 5 5 11 7 7 5 A e age Se ice ime e uck ( mm ss 19 1 9 1 33 9 1 9 1 A e age Se ice ime e o -O uck ( mm ss 1 3 1 5 5 3 37 A e age Se ice ime o ick-U uck ( mm ss 9 7 11 37 9 9 9 7 1 ime ee e o ocess a o s ( mm ss 1 9 1 13 1 1 1 17 1 1 1 1 3 1 1 o a is a ce a e e y S a e Ca ie s (mi es 51 9 5 1 99 7 5 91 13 9
Table A.11 Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age Wai i g ime e uck ( mm ss 1 31 9 3 5 5 1 5 3 1 A e age Se ice ime e uck ( mm ss 3 55 7 9 5 7 33 7 5 39 A e age Se ice ime e o -O uck ( mm ss 39 1 3 5 17 5 5 A e age Se ice ime o ick-U uck ( mm ss 5 7 31 7 19 7 1 7 55 1 5 ime ee e o ocess a o s ( ou s 1 5 1 13 1 1 3 1 17 1 1 3 1 1 1 1 o a is a ce a e e y S a e Ca ie s (mi es 5 53 91 37 9 3 9 3 79 5 9 105
Table A.12 Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
7 S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age Wai i g ime e uck ( mm ss 1 1 3 3 3 9 9 7 5 5 A e age Se ice ime e uck ( mm ss 3 5 5 5 7 5 7 A e age Se ice ime e o -O uck ( mm ss 3 3 3 3 3 5 7 A e age Se ice ime o ick-U uck ( mm ss 3 1 9 5 7 7 39 39 ime ee e o ocess a o s ( mm ss 1 39 1 1 1 1 9 1 17 1 1 3 1 1 1 1 o a is a ce a e e y 5 7 7 1 3 5 3 73 3 3 S a e Ca ie s (mi es
Table A.13 Eig S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age Wai i g ime e uck ( mm ss 1 3 3 1 3 37 33 5 1 5 53 A e age Se ice ime e uck ( mm ss 5 5 5 51 1 5 7 15 9 A e age Se ice ime e o -O uck ( mm ss 31 3 7 3 1 3 5 5 3 A e age Se ice ime o ick-U uck ( mm ss 7 5 11 31 7 9 7 31 5 ime ee e o ocess a o s ( mm ss 1 31 1 1 1 1 1 9 1 17 1 1 5 1 1 5 1 1 o a is a ce a e e y 99 33 7 7 73 1 73 7 71 3 7 1 S a e Ca ie s (mi es 106
A.3 Heuristic with Implicit Enumeration
Table A.14 ou S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io a i g e io (mi 3 5 7 9 1 A e age uck Wai i g ime o se ice ( mm ss 1 5 37 5 31 53 1 31 3 11 9 5 A e age se ice ime e uck ( mm ss 5 3 3 57 3 5 31 7 3 A e age se ice ime o o -o uck ( mm ss 33 1 1 35 3 3 3 39 3 9 51 7 A e age se ice ime o ick-u uck ( mm ss 5 3 39 33 5 7 3 1 3 7 ime ee e o i is a o s ( mm ss 1 5 1 1 51 1 1 51 1 1 51 1 17 1 7 1 1 5 11 3 19 o a is a ce a e e y S a e Ca ie s (mi es 5 93 75 5 1 7 71 9 5
Table A.15 i e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
5 S a e Ca ie s i O e a io
a i g e io (mi
3 5 7 9 1 A e age uck Wai i g ime o se ice 1 51 3 55 37 31 1 1 5 7 ( mm ss A e age se ice ime e uck ( mm ss 3 1 3 1 33 9 33 9 3 33 7 5 3 A e age se ice ime o o -o uck ( mm ss 59 3 35 5 3 3 33 9 5 A e age se ice ime o ick-u uck ( mm ss 3 31 3 3 7 7 5 33 37 3 5 7 9 5 ime ee e o i is a o s ( mm ss 1 13 1 1 5 1 1 51 1 1 51 1 17 1 1 51 1 1 51 1 1 51 o a is a ce a e e y Straddle Carriers (miles) 3 59 15 9 11 9 3 3 77 7 7 1 I 0
bl A. 6 Si S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
S a e Ca ie s i O e a io
a i g e io (mi
3 5 7 9 1
A e age uck Wai i g ime o se ice 13 3 33 3 1 13 5 3 19 19 ( mm ss A e age se ice ime e uck ( mm ss 15 51 3 3 35 1 5 5 5 1 1 51 3 A e age se ice ime o 1 5 15 51 3 15 5 9 3 39 3 o - o uck ( mm ss A e age se ice ime o 1 13 3 3 3 1 5 9 1 5 ick- u uck ( mm ss ime ee e o i is a o s ( mm ss 1 13 1 1 51 1 1 51 1 1 51 1 17 1 1 51 1 1 51 1 1 51 o a is a ce a e e y S a e Ca ie s 5 5 13 9 93 55 9 59 33 59 7 3 ( mm ss
bl A. Se e S a e Ca ie s i O e a io wi a a i g o i o o a yi g u a io
7 S a e Ca ie s i O e a io
a i g e io (mi
5 7 9 1 A e age uck Wai i g ime o se ice 1 37 1 1 1 59 15 7 17 3 19 3 1 9 ( mm ss A e age se ice ime e uck ( mm ss 1 5 1 1 19 1 17 55 19 11 1 3 15 A e age se ice ime o 1 3 15 51 17 15 5 19 3 3 9 3 o - o uck ( mm ss A e age se ice ime o 15 1 37 19 1 1 19 9 1 51 3 59 ick-u uck ( mm ss ime ee e o i is a o s ( mm ss 1 13 1 1 51 1 1 51 1 1 5 1 17 1 1 51 1 1 51 1 1 51 o a is a ce a e e y S a e Ca ie s (mi es 55 9 51 1 3 13 5 1 5 73 59 35 7 7 35 08
bl A. 8 Eight Straddle Carriers in Operation with a Planning Horizon of Varying Duration
8 Straddle Carriers in Operation
Planning Period (min)
3 5 7 9 1
A e age uck Wai i g ime 13 33 11 1 3 13 3 7 1 59 3 3 o se ice ( mm ss A e age se ice ime e 15 13 15 1 15 1 truck (hh:mm:ss) 19 7 31 5 5 Average service time for 1 15 1 35 15 5 1 5 1 5 3 1 9 drop - off truck (hh:mm:ss) Average service time for 15 53 13 1 19 15 7 7 19 5 57 pick- up truck (hh:mm:ss) Time Needed To finish a 1 13 1 1 51 1 1 51 1 1 5 1 17 jobs (hh:mm:ss) 1 1 51 1 1 51 1 1 5 Total Distance Traveled by 5 75 55 37 5 Straddle Carriers (miles) 5 5 51 77 3 59 5 APPENDIX B
RESULTS OF THE EXPERIMENT
B.1 Optimal Solution for Different Arrival Rates
Table B.1.1 Three Straddle Carriers in Operation For Truck Arrival Rate of 25 trucks/hour
3 S a e Ca ie s i O e a io a i g e io (mi 1 3 5 7 9 1 A e age uck Wai i g ime 1 1 11 3 17 3 1 o se ice ( mm ss 3 1 1 9 1 39 37 A e age se ice ime e uck 5 3 1 7 5 ( mm ss 3 9 7 1 9 5 13 1 31 1 3 3 1 3 5 A e age se ice ime o o - 1 5 3 1 o uck ( mm ss 57 31 9 5 1 1 57 3 1 A e age se ice ime o ick- 5 7 3 9 1 5 17 3 u uck ( mm ss 5 9 7 1 19 1 1 5 ime ee e o mis a o s 1 11 1 1 1 15 1 3 7 1 7 1 ( mm ss 1 37 57 11 13 19 11 5 15 1 3 9
O e ime( mm ss 11 1 15 3 7 7 37 57 1 13 19 1 5 15 3 9
o a is a ce a e e y 391 39 5 3 9 S a e Ca ie s (mi es 391 35 3 5 13 115 17 3 3 5 975 1 9 5
o a O e a i g Cos ($/ ay 7 9 1 731 7 35 731 9 7 37 79 7 1 7 77 9 91 5 3 3 1
o a Ow e s i Cos ($/ ay 1517 1
o a Cos ($/ ay 13 1 9 3 7 1 35 7 93 75 79 9 1 13 9337 7 9597 7 9 9 1 Table B.1.2 Si S a e Ca ie s i O e a io o uck A i a a e o 5 ucks/ ou S a e Ca ie s i O e a io a i g e io (mi 1 3 5 7 9 1 A e age uck Wai i g ime o se ice 1 3 3 1 5 1 11 5 5 1 1 57 1 59 9 ( mm ss A e age se ice ime 5 5 53 5 1 7 9 1 19 33 1 9 39 e uck ( mm ss A e age se ice ime o o -o uck 1 33 3 1 1 5 3 9 1 1 7 3 59 ( mm ss A e age se ice ime o ick-u uck 5 1 7 9 33 1 11 3 5 59 3 1 3 1 9 53 ( mm ss ime ee e o i is 1 3 3 1 1 1 5 1 7 31 1 1 1 3 3 11 3 1 11 5 1 1 3 5 13 a o s ( mm ss
O e ime ( mm ss 3 3 1 1 5 7 31 1 3 3 1 3 1 1 5 1 3 5 3
o a is a ce a e e y S a e Ca ie s 779 1 77 7 7 79 7 9 791 5 1 7 9 5 91 9 1 75 7 5 (mi es o a O e a i g Cos 15 1 1 959 7 15539 73 1 31 71 1 35 97 1 51 1 7 1 37 7 1 5 3 1 3 ($/ ay
o a Ow e s i Cos 99 5 ($/ ay
o a Cos ($/ ay 1 5 3 1 73 99 1 7 5 1 7 3 17111 1 7 19171 1 19 7 19 9 3 191 5 99 Table B.1.3 Eig S a e Ca ie s i O e a io o uck A i a a e o 75 ucks/ ou
S a e Ca ie s i O e a io
a i g e io (mi 1 3 5 7 9 1 A e age uck Wai i g ime o se ice 11 3 55 5 1 9 3 3 7 3 1 19 1 1 5 7 9 5 ( mm ss A e age se ice ime 5 5 7 31 1 1 3 31 53 7 1 1 33 1 37 5 1 37 55 e uck ( mm ss A e age se ice ime o o -o uck 1 5 9 3 3 3 13 9 7 3 35 13 5 5 ( mm ss A e age se ice ime o ick-u uck 5 59 5 35 3 5 13 9 55 5 1 1 3 11 1 ( mm ss ime ee e o i is 1 5 1 1 9 1 1 1 13 1 5 3 1 5 11 5 11 5 9 1 15 13 33 1 3 9 a o s ( mm ss O e ime ( mm ss 5 1 9 1 1 13 5 3 5 5 1 5 9 15 3 33 3 9 o a is a ce a e e y S a e 1173 3 11 53 11 1 7 1 3 13 7 13 1 5 13 13 1 97 1 5 15 Ca ie s (mi es o a O e a i g Cos 13 1 97 131 5 13 7 351 7 3 9 5 3 7 5573 3 9 55 9 57 5 1 ($/ ay o a Ow e s i Cos 3 5 ($/ ay o a Cos ($/ ay 7 3 3 1 11 5 17 15 3 9 7 3 13 7 3 9 3 7 5 bl . .4 e S a e Ca ie s i O e a io o uck A i a a e o 1 ucks/ ou
1 S a e Ca ie s i O e a io
a i g e io (mi 1 3 5 7 9 1 A e age uck Wai i g ime o se ice 5 1 7 1 15 3 57 5 1 1 3 35 13 5 ( mm ss A e age se ice imeme 15 5 9 59 19 9 1 5 35 1 1 1 1 1 51 5 1 7 e uck (hh:mm:ss) A e age se ice ime o o -o uck 1 5 1 57 1 5 1 7 1 5 37 33 1 37 ( mm ss A e age se ice ime o ick-u uck 33 5 3 1 5 1 3 5 1 15 1 1 57 1 3 3 3 ( mm ss ime ee e o i is 1 1 1 5 1 1 1 1 51 9 a o s ( mm ss 11 1 11 5 55 1 9 1 13 9 5 1 1 17 1 1 9 O e ime ( mm ss 1 5 1 1 51 9 1 1 1 5 55 9 1 3 9 5 1 17 1 9 o a is a ce a e e y S a e 1515 93 1511 1 1 3 75 1753 1 1791 3 1 53 75 1 7 17 1 5 1593 Ca ie s (mi es o a O e a i g Cos ($/day) 7359 7 73 9 33 31 3 33 33 7 79 33 59 7 33 1 1 33 75 5 3 53 57
o a Ow e s i Cos ($/ ay 3 31 75
o a Cos ($/ ay 31191 3113 17 33 5 3 35 3 3 3 1 37 39 5 37 91 37 5 3 9 7 3 5 33 Table B.1.5 Thirteen Straddle Carriers in Operation For Truck Arrival Rate of 125 trucks/hour
13 S a e Ca ie s i O e a io
a i g e io (mi 1 3 5 7 9 1 A e age uck Wai i g ime o se ice 3 1 13 1 7 1 15 5 3 9 51 5 1 11 15 39 ( mm ss A e age se ice ime 1 3 5 37 e uck ( mm ss 1 5 1 31 37 3 5 55 1 1 57 1 9 1 9 A e age se ice ime o o -o uck 1 1 1 1 3 11 39 1 39 1 7 3 ( mm ss A e age se ice ime o ick-u uck 19 57 5 59 11 5 1 59 1 1 3 15 1 7 5 11 (hh:mm:ss) ime Needed o i is 1 3 1 5 7 all jobs (hh:mm:ss) 1 5 1 31 15 1 5 3 11 7 1 9 13 5 13 59 3 1 3 5
Overtime (hh:mm:ss) 3 5 7 5 31 15 5 3 1 7 9 3 5 3 59 3 3 5
o a is a ce a e e y S a e 1 3 1 1 53 1 19 5 1 3 9 9 3 1 11 15 5 1 15 Ca ie s (mi es o a O e a i g Cos 3357 7 33513 9 339 93 3 7 1 3 19 1 3 1 5 5 1 1 1 5 1 11 7 9 ($/day) Total Ownership Cos ($/day) 9 1 7
Total Cost ($/day) 3 5 3 9 37 3 9 1 1 57 9 3 5 7 7593 71 35 9 75 39 7 5 114
B.2 Simulation Results for Truck Arrival Rates Ranging from 25 Truck per Hour
to 125 Trucks per Hour
B.2.1 Results for Truck Arrival Rate of 25 trucks per hour
The total cost of operation per day, shown in Figure B,2.1, is an average cost obtained from ten simulation runs. For example, the $ 8754.79 cost of operating for a fleet size of 5 straddle carriers dispatched every five minutes is the average cost of ten simulations,
Figure B.2.1 Total cost of operation per day for arrival rate of 25 trucks per hour
Figure B.2.1 shows that the optimal operation in terms of minimizing the daily cost of operation is obtained with three straddle carriers and with the assignment decision being made every five minutes. As the duration of the planning period increases from 1 to 10 minutes the cost of operation with 3 straddle carriers starts to increase after the planning period has increased beyond 5 minutes.
11
igu e 3 ime e a se u i a ucks a e ocesse o a i a a e o 5 ucks e ou
Simu a io esu s ase o uck A i a a e o 5 ucks e ou
igu e s ows a i c easi g e a e age uck a i a a e o 5 ucks e ou esu e i a o ima assig me sce a io o a i g s a e ca ie s i o e a io wi is a c i g ecisio ei g ma e e e y ou mi u es e i c ease i e a i a a e esu e i a i g 3 a i io a s a e ca ie s o accommo a e eques s o se ice a esu e i e uci g e u a io o e a i g
e io so a e assig me ca e e a ua e mo e eque y e o e a io wi s a e ca ie s equi es a i io a 7 mi u es a 31 seco s i o e ime i o e o ocess a ucks a i e u i g e egu a o e a i g ou s (A e i a e 1 117
Figure B.2.4 o a cos o o e a io e ay o a i a a e o 5 ucks e ou
As e u a io o e a i g e io i c eases o e am e o e o e a io wi s a e ca ie s a e a i g e io u a io eyo mi u es o o e a io wi 7 s a e ca ie s a e a i g e io u a io eyo mi u es e cos o o e a io i c eases e o a
is a ce a e e y s a e ca ie s i c ease sig i ica y a us esu i g i cos i c ease A so
e o e ime equi e o ocess emai i g ucks i c ease esu i g i 5 a i io a cos o s a e ca ie o e a o s wages ( igu e 5 118
Figure B.2.5 C a ge i cos o e o e a io wi ee si e o s a e ca ie s
e a e age uck wai i g ime o se ice is 3 mi u es a 1 seco s is is a acce a e wai i g ime o ucke s (A e i a e 1 A so om igu e i ca e o se e a
e a e age uck wai i g ime as a i c easi g e as e a i g e io u a io i c eases
Figure B.2.6 A e age uck wai i g ime o se ice o a i a a e o 5 ucks e ou
r .2. ime e a se u i a ucks a e ocesse o a i a a e o 5 ucks e ou
igu e 7 s ows e c a ge i o a ime equi e o ocess a ucks o e s a e ca ie ee si e wi ess a 9 s a e ca ie s e c a ge i o a ime e a se o ocess ucks as a i c easi g e as e a i g e io i c eases
.2. S l t n lt d n r Arr v l t f tr p r h r
o e uck a i a a e o 75 ucks e ou e ca i a e um e o s a e ca ie s i o e a io was i e a ge om o 1 e owes ai y cos o o e a io is ac ie e wi s a e ca ie s a wi e a i g e io o mi u es ( igu e e a e o se e i e case o 5 a
5 uck e ou a i a a e emai s A i c ease i e a i a a e equi es a i g o a i io a s a e ca ie s w ic a e ow ei g is a c e i a s o e a i g e io 20
r .2.8 o a cos o o e a io e ay o a i a a e o 75 ucks e ou
e a e age uck wai i g ime o se ice is mi u e a 11 seco s (A e i a e
1 3 A e age wai i g ime a ime equi e o ocess a ucks ( igu es 9 a 1 a e
a i c easi g e as e a i g e io c a ges om 1 o 1 mi u es
r .2. A e age uck wai i g ime o se ice o a i a a e o 75 ucks e ou 2
r .2. 0 ime e a se u i a ucks a e ocesse o a i a a e o 75 ucks e ou
.2.4 S l t n lt d n r Arr v l t f 00 tr p r h r
e o ima so u io o e a i a a es o 1 ucks e ou was ac ie e wi ee si e o 1 s a e ca ie s a wi e u a io o e a i g e io o mi u es ( igu es 11
r .2. o a cos o O e a io e ay o a i a a e o 1 ucks e ou 122
A e age uck wai i g ime a ime e a se u i a ucks a e ocesse a e s ow o igu e
1 a igu e 13 es ec i e y
Figure B.2.12 A e age uck wai i g ime o se ice o a i a a e o 1 ucks e ou
Figure B.2.13 ime e a se u i a ucks a e ocesse o a i a a e o 1 ucks e ou 2
.2. S l t n lt d n r Arr v l t f 2 tr p r h r
e a i a a e i c ease om 1 ucks e ou o 1 5 ucks e ou esu e i a i g
wo a i io a s a e ca ie s o accommo a e ew se ice eques s e a i g e io u a io
emai e e same a mi u es igu e 1 s ows e c a ge i e o a ai y cos as a u c io o e a i g e io a s a e ca ie ee si e e mi imum cos is ac ie e wi 13 s a e ca ie s i o e a io ei g co si e e o assig me e e y mi u es
r .2. 4 o a cos o o e a io e ay o a i a a e o 1 5 ucks e ou
o ocess a ucks s a e ca ie s a e emai i g i o e a io o a i io a 5 seco s
co si e e as o e ime A e age uck wai i g ime a ime e a se u i a ucks a e ocesse a e
s ow o igu e 15 a igu e 1 es ec i e y 24
r .2. A e age uck wai i g ime o se ice o a i a a e o 1 5 ucks e ou
r .2. 6 ime e a se u i a ucks a e ocesse o a i a a e o 1 5 ucks e ou E E E CES
e e a M iamo C u a a o e G e im ac o o assig me u es o s a e ca ie s o e oug u o co ai e e mi a s ou a o A a ce a s o a io o 3 o 3 15-
ose ei e s i k S ee ke a S e a oss e ic e is a c i g a sea o co ai e e mi a s usi g e o u io a y a go i ms ocee i gs o e 33 awaii I e a io a Co e e ce o Sys ems Scie ces
isko a A e a S a ma I e o y ase is a c i g o au oma e gui e e ic es o co ai e e mi a s
u eau o a s o a io S a is ics //www s go / u ica io s/ame icas_co ai e _ o s (Accesse Se em e 15 7
C ao iu i Sa c oy as a a S aso ic A a assious a ikas us a a ki a C e Ya g 199 O imi i g e o e a io s o a co ai e o a io a Ce e o a s o a io a I us ia o uc i i y ew e sey I s i u e o ec o ogy
as S a S aso ic 3 Sc e u i g ma e ia a i g e ic es i a co ai e e mi a o uc io a i g & Co o o ume 1 o 7 3 — 33
ow a M esc i e Co ai e e mi a o uc i i y A e s ec i e // e as-sea- g a amu e u/ u s/ o s Was i g o /Co ai e e mi a o uc (accesse Se em e 7
oo Me i M 195 e a e i g sa esma o em O e a io s esea c o 1- 5
oy a G Koc Megow E ua e a W e O imi i g e a si e o e a io o a co ai e e mi a I - e o -
a am e_G i i a Me issa Mu y Co ai e e mi a o uc i i y E e ie ces a e o o o g A ge es a o g eac
a a am i es E Com e i io e cess ca aci y a e ici g o o i as uc u e I e a io a ou a o Ma i ime Eco omics o 3 3-3 7
a a am i es E Ca iou a M e acc io Cos s e e i s a ici g o e ica e co ai e e mi a s I e a io a ou a o Ma i ime Eco omics o 1-3
1 5 26
a o W Ku 1955 e u ga ia me o o e assig me o em" a a esea c ogis ic Qua e y o 3-97
a ma S A ge e a amewo k o sc e u i g equi me a ma owe a co ai e e mi a O S ec um 51-7
ua g W C a C Y C u 3 Cos com a iso o s a e ca ie i ec a e ay sys ems i co ai e e mi a s ou a o Ma i e a ec o ogy o 11 o 197-
31 G ou I a co ai e e mi a a a ysis i a e o
I s i u e o S i i g Eco omics a ogis ics Wo sea o e co ai e a e a o a ic e o www.isl.org (Accesse u e 1 th 7
I e a io a I e mo a a s o a io Ce e a I E e e ou s o o e a io a e o aci i ies i ew e sey A easi i i y A a ysis
Ioa au A u a C-I iu K uka i o ic ou mo amma i a E oug e y 1 A a ce ma e ia a i g au oma e gui e e ic es i agi e o s Ce e o Comme cia e oyme o a s o a io ec o ogies
Kim K a K Y Kim 1999 ou i g s a e ca ie s o e oa i g o e a io o co ai e s usi g a eam sea c a go i m Com u e s a I us ia E gi ee i g 3 (1 1 9-13
Kim K Y a K Kim eu is ic a go i m o ou i g ya -si e equi me o mi imi i g oa i g imes i co ai e e mi a s
Ko a E a es o Ma ema ica mo e i g o co ai e a s e s a s o age oca io s a sea o e mi a s O S ec um 519-537
Kuma A Mo i ie me o o so i g e u a a ce assig me o ems A ie Ma ema ics a Com u i g o 17 Issue 1 7 -
Mee sma a e oo e E a a eIs a e a s o o ec i es i s ume s a i ica o s
Mee sma s a A Wage ma s 1 E ec i e a go i ms o i eg a e sc e u i g o a i g equi me a au oma e co ai e e mi a s Eco omics I s i u e e o EI 1-19
Mo ok E wa K Sammo o S aso ic a a a o ick i a K 1995 Im o i g o uc i i y i i e mo a ai - uck a s o a io I a ke (E e se ice o uc i i y a qua i y c a e ge ( 7- 3 o ec e e e a s K uwe Aca emic u is e s 1 7
Munkres, James. 1975.Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, Vol.5, No,1. pp.32-382,
Murty, L. and L, Wan.2003, A decision support system for operations in a container terminal.
National Center for Transportation and Industrial Productivity (NCTIP) at the New Jersey Institute of Technology,2003, Mobility and the costs of congestion in New Jersey — 2003 Update",Final Report prepared for the Blue Ribbon Commission and the New Jersey Department of Transportation.
Nijkamp, Peter, Enno Masurel and Casper Burgering. 2002. Economic Feasibility of New Generation Terminals Emerging Perspective. European Transport, Issue 22.
Particle Data Group, 2008. The review of particle physics, http://pdg.lbl.gov/2008/download/rpp-2008- p1B667,pdf (Accessed October 2008).
Port Authority of New York and New Jersey, hftp://www.panynj.gov/DoingBusinessWith/seaport/html/tradestatistics.html (Accessed June 10 2007),
Port Authority of New York and New Jersey, http://www.panynj,gov/DoingBusinessWith/seaport/pdfs/2007Trade_Stats_R4.pdf ( Accessed June 10 2007).
Port of Tacoma, 2008, http://www.portoflacoma.com/page,aspx?cid=83 (Accessed November 2008).
Rashidi, Hassan and Edward P.K. Tsang. 1999. Container terminals: scheduling decisions, their formulation and solutions. Submitted to Journal of Scheduling.
Report by the UNCTAD Secretariat. 1995. Strategic port pricing.
Saanen, Yvo, Jeroen Van Meel and Alexander Verbraeck. 2003, The design and assessment of next generation automated container terminals, Delft University of Technology.
Secretariat of UNCTAD, 1985.Port development, United Nations Conference of Trade and Development.
Sgouridis, S.P, and D.C. Angelides, 2002, Simulation-based analysis of handling inbound containers in a terminal," wsc,pp.1716-1724, 2002 Winter Simulation Conference (WSC'02),
Spasovic, L. and A. Sideris. 1992. Increasing productivity and service quality of the straddle carrier operations at a container port terminal.
Steenken, Dirk, Andreas Hennings, Stefan Freigang and Stefan VoB. 1993. Routing of straddle carriers at a container terminal with the special aspect of internal moves. OR Spectrum 15: 167-172, 128
S ee ke i k S e a o a o e S a ock Co ai e e mi a o e a io a o e a io s esea c — a c assi ica io a i e a u e e iew O S ec um 3- 9
sa g E wa 199 AI sc e u i g ec ique – a com a a i e s u y
acca I a ia Mic ae ie ai e a Ma eo Sa a i 7 O imi a io a co ai e e mi a s s a us e s a e s ec i e Swiss a s o esea c Co e e ce Se eme e 1 th o 1 th
ie am Sea o Associa io ( A //www a o g /E G IS / ai i g/ e mi a ew / e mi a _O e a io _CaseI_I (Accesse o em e
is I a M akke 5 is a c i g a ayou u es a a au oma e co ai e e mi a