SIMULATION OF BAGGAGE HANDLING AT

VANCOUVER INTERNATIONAL AIRPORT

by

Martin Lloyd Elliott

B. Sc Queen's University, 197 5

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

in

THE FACULTY OF GRADUATE STUDIES

(Management Science Division)

Faculty of Commerce and Business Administration

We accept this thesis as conforming

to the required standard

THE UNIVERSITY OF BRITISH COLUMBIA

July, 1977

Martin Lloyd Elliott In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study.

I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.

Department of /JAft/ZKi^ngA/T Sc-ietjce „ fftcuvn Of Coh.Hefi.cC

The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5

Date I i

Abstract

This thesis details a simulation study of the domestic deplaning passenger and baggage subsystem at Vancouver International Airport

The present and future baggage systems are described and modeled and the model is then validated., The capacity of the baggage claim area is estimated. The use of this simulation model as a tool for inexpensively investigating the effect of different flight schedules on queues in the baggage claim area is demonstrated. The simple alterations necessary to apply this model to different airports are also described. ii

Table Of Contents

abstract . I

List Of Tables ...... v

List Of Figures And Illustrations ...... vi

Acknowledgement ...... ix

1 INTRODUCTION ...... 1

1.1 Literature fleviea Of Existing Models ...... 3

2 PROBLEM DEFINITION 8

2.1 Overall Description ...... 8

2.2 Airport Flowchart ...... 10

2.3 Symptoms Of The Problem ...... 13

2.3.1 Congestion ...... 13

2.3.2 Airport Level-of-service ...... 14

2.3.3 Delay Distributions ...... 15

2.4 Choice Of Method 17

2.5 The Simulation Of The Airport System ...... 19

3 FORMULATION OF THE SIMULATION .20

3.1 Methodology 20

3.2 The Present System ...... 22

3.3 Changes In The System ...... 26

3.4 Model Requirements ...... 28 iii

3.5 Data Requirements ...... 29

3.5.1 Input Data 29

3.5.2 Output Data 32

3.5.3 Methods Of Data Collection ...... 34

3.6 Formulation Of Time Oriented Mathematical

Model ...... 35

3.7 Formulation Of Computer Model 37

3.7.1 Simulation Language ...... 40

3.7.2 Scope Of The Model ...... 43

3.7-3 Model Logic ...... 44

3.7.4 The Program ...... 54

4 SIMULATION RESULTS ...... 56

4.1 Validation ...... 56

4.2 Simulation Experiments ...... 63

4.3 Analysis And Results Of Simulation ....71

4.4 Conclusions ...... 93

5 FOOTNOTES 95

6 BlBLIOGRAPHY ...... 96 i v

7 APPENDIX

a discussion of airport data collection .,,.....97

b suggested areas of further research ...... 98

c addition of international arrivals module ....100

d flowchart ...... 102

e listing of program ...... 110

f program output ...... 120

g a quick user's guide ...... ,,.,....131 V

List Of Tables

Table I Service Priorities Of Business And Pleasure

Travellers ...... 2

Table II Facility Input Data ...... 29

Table III Traffic Input Data ...... 30

Table IV Operational Input Data ...... 30

Table V Traffic Flow Regions Of Interest ...... 32

Table VI Occupancy Measures ...... 33

Table VII Summary Table Of Data Contained In Model .....44

Table VIII Validation Results Of 3 Sample Flights ...... 57

Table IX Scheduled Interarrivals For 95%

Confidence Interval ...... 90 vi

List of Figures and Illustrations

Illustration 1 Vancouver airport ...... 9

Illustration 2 747 And L-1011 Minimum Container Handling

Times ...... 25

Illustration 3 Capsule Flowchart ...... 42

FIGURE 1 Airside Functional Flow Block Diagram ...... 10

FIGURE 2 The Output Of The Airport Landside Analysis ..14

FIGUHE 3 Probability Of Passenger Delay ...... 16

FIGURE 4 General Features Of An Airport

Simulation Model ...... 19

FIGURE 5 Methodology For Developing The Airport Landside

Simulation - Model ...... 21

FIGURE 6 The Path Of Passengers And Bags ...... 23

FIGURE 7 Map Of Carousel Area 27

FIGURE 8 Typical Inputs And Outputs Of A Time Oriented

Queueing Model ...... 35

FIGURE 9 Illustrative Time Flow Analysis ...... 39

FIGURE 10 Carousel Availability ...... 48

FIGURE 11 Cost Of Program Run As A Function

Of Number Of Transactions ...... 55

FIGURE 12 Last Bag On DC-9 Flights At Gate 4

Vs Number Of Passengers ...... 61

FIGURE 13 Last Bag On 747»s And L-1011»s At Gate 10

Vs Number Of Passengers ....62

FIGURE 14 Simulated Waiting Time For Passengers vii

To Receive all Bags In Carousal firea ...... 65

FIGURE 15 Simulated Waiting Time For all Bags Of

A Passenger To arrive 66

FIGURE 16 Simulated Passenger Transit Time And

Bag Transit Time And.Matching Process ....,.,.67

FIGURE 17 Varying Conveyor Speeds ...... 72

FIGURE 18 Varying Unloading Rates Of Bags (DC-9) ...... 74

FIGURE 19 Varying Unloading Rates Of Bags {747) ...... 74

FIGURE 20 Average Waiting Time At Positive Claim

Checkpoint Vs Number Of Passengers In

Baggage Claim Area ...... 76

FIGURE 21 Bag Delivery For Two 747«s (340 Passengers)

Arriving At Varying Time Intervals For CP AIR

On Present Carousel System 78

FIGURE 22 Bag Delivery For Two 747*s (240 Passengers)

Arriving At Varying Time Intervals For CP AIR

On Present Carousel System ...... 79

FIGURE 23 Bag Delivery For Two 747's (440 Passengers)

Arriving At Varying Time Intervals For CP AIR

On Present Carousel System .,...,...,...,.....80

FIGURE 24 Bag Delivery For Two 747*s (340 Passengers)

Arriving At Varying Time Intervals For Air Canada

On Present Carousel System ...... 82

FIGURE 25 Bag Delivery For Two 747 *s (24 0 Passengers)

Arriving At Varying Time Intervals For Air Canada

On Present Carousel System 83

FIGURE 26 Bag Delivery For Two 747»s (440 Passengers)

Arriving At Varying Time Intervals For Air Canada viii

On Present Carousel System ...... 84

FIGUBE 27 Bag Delivery For Two 747»s (340 Passengers)

Arriving Simultaneously And For The Arrival Of A

DC-9 (100 Passengers) At Varying Intervals

For Air Canada On New Carousel System ...... 87

FIGURE 28 Bag Delivery For Two 747's (240 Passengers)

Arriving Simultaneously And For The Arrival Of A

DC-9 (100 Passenger's) At Varying Intervals

For Air Canada On New Carousel System ..88

FIGUBE 29 Bag Delivery For Two 747's (440 Passengers)

Arriving Simultaneously And For The Arrival Of A

DC-9 (100 Passengers) At Varying Intervals

For Air Canada On New Carousel System ...... 89

FIGURE 30 Simulated Arrival Intervals Between Two Air

Canada 747's On Present Carousel System 91

FIGURE 31 Simulated Arrival Intervals Between Two CP

AIR 74 7's On Present Carousel System ...... 91

FIGURE 32 Simulated Arrival Intervals Between Two

747»s And A DC-9 (100 Passengers) For

Air Canada On New Carousel System ...... 92 Acknowledgement

The author wishes to gratefully acknowledge the assistance and support received from many people during the 7 months of research and writing.

Particular thanks are given to the chairman of the author's thesis committee, Professor Dean Oyeno of the

Management Science Department at OBC. His suggestions, criticism and enthusiasm were of great help.

Thanks are also due to the other two members of the author's thesis committee. Professor Derek Atkins of the

Management science Department at UBC and Professor Albert Dexter of the Accounting and Management Information Systems Department at UBC each gave very useful comments and criticism to the author.

Others who gave their time and help were Mr. Ken

Krauter, Mr., Bert Saumur and Mr., Norm Street, all of the

Vancouver Airport Master Planning Team {Transport Canada), Mr.

Davisson, Assistant Airport Manager, Mr. George Popeniuk of the

Operations Department, Mr. Bill Shilvock, station Manager of CP

AIR who allowed me to observe the CP baggage handling system, and Mr. Bob Baillie, Air Canada Quality Assurance

Representative who provided me with rauch information and validation data.

Thanks are also given to the airlines which replied to the author's written enquiries: Eastern Airlines, United

Airlines, American Airlines, and Delta Airlines. 1

1 INTRODUCTION

Air transportation has made the world seem much smaller by dramatically reducing long distance travel time.

Airline passengers can now travel from Montreal to Vancouver in five hours air time. Further reduction in total travel time is constrained by airport delays which in certain instances are comparable to air travel time. To obtain significant additional savings in overall travel time with the accompanying dollar savings and other benefits, it is necessary to reduce airport delays.

Most airport delays are due to airport landside congestion ( landside being defined as that part of the airport

from the airport entrances/exits to the arrival/departure gates). In particular, one of the major sources of delay in the domestic section of the airport is in the deplaning passenger

and baggage flow. The length of time required to retrieve baggage is a very common passenger complaint., According to a CP

AIR survey, baggage delivery to passengers ranks in the top 4 of

service priorities { see TABLE I )*.

Computer simulation offers an effective and realistic

approach to further study of the airport landside problem as a system. Because of the complex dynamic nature of the problem,

computer simulation may be the only feasible and viable approach. This thesis describes how simulation of the deplaning

domestic passenger and baggage flow can be used to accurately determine the landside flow and associated delays. Attainment

of this goal will reguire validating the computer model against 2

the system it simulates.

TABLE I

SERVICE PRIORITIES OF BUSINESS AND PLEASURE TRAVELLERS

Ranking Ranking ( All Passengers ) Business Pleasure

1 Onetime Departure/arrivals 1 3 2 Rapid, Courteous Airport Check-in 3 2 3 Fast Baggage Delivery At Destination 2 4 4 Prompt, Courteous Reservation System 4 1 5 Flight Delay Advice And Assistance 5 5 6 Appearance/attitudes Of Flight Attendants 6 6 7 Seat Selection At Airport Check-in 7 8 8 Assistance From Personnel On Arrival/departure 8 7 9 Smoking/no Smoking Sections 11 9 10 Full Traditional Meal Service 9 10 11 Reading Material In-flight 12 11 12 Light Meals 14 12 13 In-flight Bar Service 10 14 14 In-flight Wine/ligour 13 16 15 Audio Entertainment 15 15 16 No Frill Service 16 13 17 In-flight Movies 17 17 3

1.1 LITERSTORE BEVIEI OF EXISTING MODELS

Extensive research uncovered only six useful articles on the subject of simulation modeling applied to airport baggage handling. This may in part be due to the fact that much of the work in this field is being carried out in the operations research departments of competing airlines. However, it is also true that airport models are only just becoming popular and accepted as valid planning tools.

The two American airlines that gladly admitted possessing simulation models were two of the largest: United Air

Lines and Eastern Airlines. In Canada, Air Canada and the

Ministry Of Transport have used simulation models.

Kimball Mountjoy of United Air Lines states that

United has four airport simulation models 2. One model analyzes maintenance alternatives; another model, "The Airport Planning

Model", was written specifically for United's operation at

0*Hare International Airport and emphasizes the problems of flight connections under an increased schedule and of the effect the new year's schedule will have on baggage delivery time. The validation was accurate and showed for example an average 13.9 minutes for simulated bag delivery compared to an observed 13.6 minute average. One program is titled the " Taxiing And Towing

Model" and is written again for O'Hare Airport to answer such questions as "what per cent of flights will be unable to taxi directly to their gates?" The fourth model is called the

"passenger Flow Model" and deals with passenger handling within the terminal. It is designed to answer such guestions as "What 4

ticket counter and baggage check-in facilities will be needed at

a station to meet service goals?" The model is still under

development. The models are written in GPSS . The paper ends

with the statement that their experience has been with separate

airport models designed to answer specific guestions. Their

approach of detailing areas of concern and simplifying the rest

is practical and sound, particularly since experience gained in

one model can be applied to another so that they become

successively easier to write.

L.G. Klingen of Eastern Airlines describes six

simulation models currently in use (all written in GPSS) : an

Airport Runway Model, a Terminal Airside Model, a Baggage

Handling Model, a Ticket Counter Model, a Curbside Model and a

Mobile Lounge Model3. Their baggage handling model is

applicable to domestic flights only. When a flight arrives,

bags are unloaded onto the baggage cart which is then towed to

the bag claim area. This model was used to design the

parameters for Miami's automated baggage handling system.

The Canadian Ministry Of Transport collected data to

create a Fortran model of Vancouver Airport. This model

simulates the whole airport from runway to passenger exit at

airport boundary., It is expensive to run however and is not

used *.

Air Canada has a simulation model to test its flight

schedule and gate assignment. There is no model in use by Air

Canada to analyse passenger and baggage flow at Vancouver

Airport s.

Three journal articles described simulation studies of 5

baggage handling; one study was hypothetical.

The first article, by Mellichamp and Fillmer, discusses a GPSS model of baggage handling operations at Atlanta

International Airport *. This model dealt with the enplaning

module. The objective of the simulation study was to improve

the system effectiveness through modifying operating policies.

Such modifications as checking in "luggageless" passengers

directly at the gate area and having a separate check-in counter

for each of an airline's high density routes were considered.

In assessing the effectiveness of the modifications, primary

attention was given to utilization statistics for ticket

counters, the average time passengers spend in the ticket line,

and the average time an item of luggage is in the system. This

article concludes that with current planning and design problems

being as complex and involving as many variables as they do,

there are essentially only three approaches to evaluating system

modifications: subjective analysis (conjecture), trial and

error, and simulation. They claim that simulation offers the

only viable possibility for obtaining accurate assessment of

response to system modification without tampering with the real

system.

The second article, by Lui, Nanda and Browne,

discusses the development by the Port Of New York Authority of a

GPSS model designed to simulate international baggage operations

at J.F. Kennedy International Airport 7. This was a very

useful article outlining the construction of their model logic

and the various GPSS mechanisms that were employed. Particular

care was taken in the data collection and validation. Data on 6

each operation was obtained by four to five collectors using a random cluster sampling method. Data collection included periods of peak activity on all days of the week. A validation team of 26 people collected data on the entire system for two days. Analysis of results showed good correspondence and correct model logic, The article stated that at certain times, however, simulated results fluctuated more than the actual. The cause was traced to certain extreme sample values from the cumulative density function which were not sampled frequently enough to reach their expected values. As a result extreme values from these distributions were truncated. This reduced the discrepancies between the actual and the simulated to an acceptable level. The article concludes with the statement that the simulation program is used to evaluate short range expansion plans for the Internation Arrivals Building.

The third article, by G.L. fiobinson, is titled

"Simulation Models for Evaluation of Airport Baggage-handling

Systems"8. It describes the evaluation of hypothetical baggage handling systems under different baggage loads. First the article considers four performance indices for evaluation of the baggage claim area: average delay for individual passengers,

standard deviation of delay for individual passengers, maximum delay for individual passengers, and total delay for all passengers, in passenger-hours. The study chooses total delay as the most appealing criterion because such an index could be converted to an estimated dollar value of "lost" man-hours per

flight. The article was helpful as an example of a simulation study in the baggage handling field and it contained some 7

important comments on statistical analysis of simulation

results. The importance of several runs with different sets of

random numbers was stressed. The confidence interval for the

mean value of a performance index can be made as small as

desired in this way.

The last article consulted was a general article on

the " Use Of Simulation In Airports", By Dana Low9. Perhaps its

most important message was the importance of close communication

between the model user and the model builder. The article also

states that something on the order of one third of the total

time and budget permitted for simulation modeling should be

reserved for application, analysis and interpretation of

results. The article had nothing specific to say on baggage

handling models.

Collectively these references conveyed a good picture

of the current state of airport simulation modeling. 8

2 PROBLEM DEFINITION

2.1 OVERALL DESCRIPTION

An airport consists of three subsystems: the airspace (including runways), the airside (including runway turnoffs and airline gates), and the landside (including everything from the airline gates to the airport boundaries). Illustration 1 shows these subsystems at Vancouver Airport ., This thesis is concerned with the deplaning domestic passenger module within the landside subsystem.

10

2.2 AIRPORT FLOWCHART

FIGDSE 1 is a simple functional flowchart of the airport iandside activities.. Each facility (in blocks of FIGURE 1 ) may

FIGURE 1 AIRCRAFT DOOR I ZEE BAGGAGE GATE HOLDING BAGGAGE CLAIM ROOM ROOM

SECURITY

RENTAL CAR AIRLINE CHECK- IN CHECK-IN

TERMINAL BUILDING ENTRANCE/EXIT

CURBSIDE FACILITIES AIRPORT —X PARKING 1" FACILITY PARKING LOT FACILITIES 4- --4-- AIRPOR_i T 1 . ROAD FACILITY A L. AIRPORT BOUNDARY

Figure 1 Airport Landside Functional Flow Block Diagram

represent a network of subfacilities, which when linked 11

together, support the complex activities of movement and service operations.

Enplaning passenger vehicles entering the airport proceed to the parking lot for long or short duration parking, to a rental car check-in area, or to the curbside for unloading.

The passengers and visitors then proceed into the terminal. The passengers may wait in the terminal area or proceed to the ticket counter, baggage check-in , the car rental check-in counter, or to the airplane gate where they must pass through a security check before enplaning. The order in which these activities may be performed (except for enplaning) is not necessarily fixed and depends upon factors such as the nature and origin of the trip and the terminal geometry.

Except for peak periods and peak seasons such as

Christmas the passenger and baggage flow of the enplaning system generally proceeds without any long queues or waiting periods.

The main waiting problems occur for domestic passengers when the

flight has terminated and passengers must proceed to pick up

their bags. It is for this reason that this thesis concentrates on the deplaning system.

In this study, arriving passengers enter the model at the air terminal gate where they exit from the airplane.

Deplaning passengers either proceed to another flight or move

through the terminal to the airport boundary. Many passengers

will need to get their baggage at the baggage claim facilities.

The model in this thesis ends when the passenger has claimed his bags, matched the tags at the positive claim check and left the baggage claim area. The rest of his progress through the 12

landside system does not usually cause any holdups of the magnitude of 15 minutes to 1/2 hour that can occur in the baggage claim stage.

V 13

2.3 SYMPTOMS OF THE PROBLEM

2.3.1 CONGESTION

Congestion results from inadequately meeting traffic demands. Congestion can arise in two ways: (1) the traffic demands approach or exceed the capacity of the facility and (2) a service facility malfunctions or some reduction in service reduces the effective capacity of the service facility {such as only one employee matching tags at the baggage claim area) .

Congestion normally shows up as queues. Queue length may increase very rapidly as the traffic demands exceed the facility capacity. The facility capacity is defined as the maximum flow of traffic through the facility.. This is usually measured in passengers per hour (or bags per hour). Congestion is usually associated with peak hour arrival rates. as the arrival rates increase, congestion shows up as longer queues at service facilities and as an increase in associated delay times. 14

2.3.2 AIRPORT LEVEL OF SERVICE

The output of an airport Iandside analysis should be measures of the airport Iandside level of service based on specific input data (see FIGURE 2}

FIGURE 2

AIRPORT AIRPORT MEASURES OF INPUT DATA LANDSIDE AIRPORT LANDSIDE LEVEL OF SERVICE ANALYSIS ^

Figure 2 The Output of the Airport Landside Analysis

there are many possible measures of airport service such as yearly passenger flow, operations per hour and peak hour demands, depending on which service is being studied. This thesis has assumed that passenger delay for specified flow and holding times is the desired measure of level-of-service in this study. Therefore the pertinent variables considered in this study were passenger delays, gueue lengths, holding capacity of the baggage area and passenger flow., 15

2.3.3 DELAY DISTRIBUTIONS

The simulation model in this study uses empirical probability distributions to calculate service times.

Nonetheless, this section provides a brief review of the theory which forms the basis of a gueueing model.

The impact of congestion on an airline passenger can be measured by delay which, in turn can be expressed by delay distributions as described by Gordon 10. The passenger delay can thus be expressed in terms of the following functions:

a) probability distribution that passenger delay is

greater than time t.

B) probability distribution that the time to complete

facility service is greater than time t.,

C) probability distribution that the time spent

waiting for service is greater than time t.

Function (a) includes functions (b) and (c).

An example of a probability distribution of delay resulting from congestion is shown in FIGURE 3 .

FIGURE 3 presents the delay probability as a function of jj.t. parametric in p , where ju.= Mean Service Rate , p = Ratio Of

Arrival Rate To Service Bate .

The distribution function is maximum at t=0 and cannot increase with t. Therefore, one of the objectives of an airport design is to keep the probability P{0) at t-0 as small as possible. However, a low value of p{0) does not necessarily guarantee a satisfactory system since, for that, the probability at large values of t may not become insignificant. That is, the FIGURE 3 16

O UJ 0.? Z D POISSON ARRIVALS EXPONENTIAL SERVICE So 0.6 U. f* O z > < i- I

04 CE R BAB I C Z a: O a. -J 0.2

Reference io

Figure 3 Probability of Passenger Delay

"tail" of the distribution may stretch out to large values. The time corresponding to a particular likelihood level can be picked to show how far the tail stretches. For example, from

FIGURE 3 it can be seen forp=0.8 whenju>i, 30% of the waiting entities will wait longer than 5 seconds. Also forp=0.8 when

M=5, 30% of the waiting entities will wait longer than 1 second.

This measure is often called the .grade of service.

Increasing the mean arrival rate and/or decreasing the mean service rate tends to increase the probability that the passenger will spend a time greater than time t in the facility.

This can be seen from FIGURE 3 by observing that as p increases

(that is, as the ratio of arrival rate to service rate increases) the delay curves move further from the origin. This indicates an increased probability of passenger delay. 17

2.4 CHOICE OF METHOD

The determination of airport landside capacities and delays may be performed by three methods: experimentation,

analytical modeling and simulation. Although a detailed cost-

benefit analysis was not performed, the important criterion used

for selection of simulation as the method in this study was the ability to describe the detailed activities of the airport.

An experimental program which would involve a test of

the airport flow capacity by enacting peak loading processes

would be unmanageable. , The logistics of having several

thousands of participants for off-hour experiments are

overwhelming and performing the experiment during business hours

might needlessly interfere with airport landside operations.t In

addition it is not easy to apply the capacity and delay

statistics for Vancouver Airport to another airport should it be

necessary to analyse another airport.

Analytical models which are represented by solutions

of differential equations are useful in describing flows and

delays at a particular facility such as a curbside or a ticket

counter. However, the great number of interacting elements in

an airport landside complex require too many simplifying

assumptions to permit describing the detailed activities by

analytic methods. Analytic approaches are further complicated

by the independently fluctuating nature of service and arrival

rates.

The simulation method was chosen for this problem

because it is potentially able to describe the detailed 18

activities of the airport system in a meaningful, manageable

fashion. ... The simulation model can incorporate a large number of interactions. Rates may be varied in accordance with observed conditions. The simulation model opens up the possibility of easily and quickly determining the impact of changed input variables and the sensitivity of the airport Iandside operation to such changes. For example it is easy to change such variables as passenger traffic volume, bags per passenger,

number of baggage personnel, size of baggage carousel and other

Iandside elements. Simulation models require some adaptation

when applied to different airports but such adaptation is generally minor because the basic processes to be simulated are

identical from airport to airport. 19

2.5 THE SIMULATION OF THE AIRPORT LANDSIDE SYSTEM

The general features of this airport simulation are shown in FIGURE 4.

FIGURE 4

Reference 3

Facility- Outputs per| Outputs per Data Flight Airport

Traffic Data Simulation Delay Delay Input<(Including Model Statistics Data ) . Passengers) Statistics

Flow Operational Flow Statistics Data Statistics Occupancy Statistics Figure k General Features of an Airport Simulation Model

Simulation inputs are derived from observations of the specific airport to be simulated. However, some aspects such as

operational data, may remain constant from one airport to another and are applicable to any airport. The traffic data

input refers here to passengers and baggage. 20

3 FORMULATION OF THE SIMULATION

3.1 METHODOLOGY FOR DEVELOPING THE AIRPORT LAN DSIBE

SIMULATION MODEL

To establish a computer simulation model for a system as complex as an airport Iandside traffic system, for example, a clearly defined methodology is required. A simulation computer model requires in order

problem definition

model requirements

mathematical models

computer program requirements

computer program

field data

computer model validation

simulation experiments

FIGURE 5 shows these steps in flowchart form.

Problem definition should explicitly cover the objective of the work, so that model requirements can be established., The data collection is needed to provide both input data and data to be used in validation. The formulation of the mathematical model and the associated computer program should consider that the airport Iandside is a dynamic system with time-varying interactions. The simulation model must provide a measure of level-of-service . The validation effort establishes the model validity by correlating the simulation output data with measured field data and determines, if indeed, 21

FIGURE 5

DEFINITION OF PROBLEM

MODEL REQUIREMENTS

H DATA COLLECTION EFFORT I FORMULATION OF MATHEMATICAL MODELS

COMPUTER PROGRAM 1-4*1 FORMULATION OF REQUIREMENTS COMPUTER PROGRAMS i

1 r SIMULATION EXPERIMENTS

REJECT VALIDATION, OR MODIFY ACCEPT Reference 3

Figure 5 Methodology for Developing the Airport Landside Simulation Model.

the simulation model meets the model requirements. 22

3.2 THE PRESENT SYSTEM

The diagram in FIGURE 6 shows in simplified form the

path of a passenger and his bags from the airplane gate to the

Baggage claim area. Some few passengers will have no bags to

pick up and will leave the system at the airplane gate. Others

will be passengers in transit who will proceed to the departures

area of the airport and whose bags will be transferred to

another plane. Usually most of the passengers will, however,

proceed directly to the baggage claim area. The % connecting

averages to 10% on Air Canada. This figure may be set by the

user.

FIGURE 7 shows in more detail the actual network of

conveyors at Vancouver Airport . When a plane arrives at a

gate, baggage personnel open up the hold and unload.

Observation indicates that the hatch opens within 30 seconds to

1 minute {with equal probability in this interval) of the

passenger plane door opening. One person in the hold loads the

bags one by one onto a short unloading conveyor. From this

conveyor a second and sometimes a third man load the bags onto

waiting baggage trucks. Sometimes the bags are loaded directly

onto the trucks if the plane hatch is not too high off the

ground or if no belt loader is conveniently available. The rate

of unloading is the same by either method. There are usually

two cargo hatches on each plane from which bags are off loaded.

That is, each plane is usually met by at least two baggage

trucks. The number of trucks to meet an airplane is input to

the model through parameter 12 {refer to Section 3.7»3 Model FIGURE 6

The Path of Passengers and Bags

Plane Bags per Arrives Passengers Passenger Leave Plane Assigned exit rate A x 3 sec/pass./ / ,' i i ; bags Passengers

Unload bags w/o bags? onto carts Connecting (or ULD's if passengers?/ 747 or L-1011)

Leave Model 6 •? 8 sec/bag

Passengers

Drive to walk to claim area Conveyor walking speed Unload bags

v 1 onto conveyor f ft/sec

P Baggage Claim Device -passengers and bags are matched. Bags sec/bag Connecting bags leave are removed when model matcned. Move on removal conveyor to time carousel *° sec/bag rositive Claim Leave Model K Check 24

Logic) . The full trucks are then driven a short way from the

airplane gate to the baggage conveyor. An employee then unloads the bags from the trucks onto the conveyor which takes them to the carousel in the claims area.,

CP AIS has individual trucks each with a capacity of

80-90 hags. Air Canada meets a plane with two tractors each

towing a sufficient number of tubs to cope with the baggage load. Each tub has a capacity of 30-40 bags.

In the case of 747,s and L-1011*s there are containers

for baggage and cargo.. These are officially named Unit Loading

Devices or ULD'S ., When the plane arrives at Vancouver Airport

the ULD's destined for Vancouver are unloaded in pairs from each

of two cargo doors by 5-9 employees, taken to the conveyor area on carts and are then unloaded onto the conveyor leading to the

carousel.

For a visual description of the 747 and L-1011

container handling process see Illustration 2 **. .. Observations

indicated that the times shown are strictly minimum times.

Actual times vary within a range of values. Positioning

equipment for example can occur anywhere in the interval of 2 to

6 minutes. Frequently a lock or catch becomes stuck and

unloading 2 containers can take anywhere from 3 to 4 minutes.

These ULD handling times are contained in the model by sampling

from uniform distributions about the average value of the

particular handling time involved. 25

ILLUSTRATION 2

•jk-J nnri L-1011 - Minimum Container Handling, Times; • , AIRCRAFT TO TERMINALS

(i) 7U7 Forvard Hold: l6 Containers (ii) 7U7 Rear Hold: Ik Containers (iii) L-1011 Cargo Holds: 8 Containers Each

TIME FRAME FOR B-7>*7 REAR HOLD Wheel Stop Pos'n Equip.

°Pe9oS?iso Load 2'ULD's Raise Platfmv Lower Plat f HIT Load to Dolle Transit Xi Termina to =-l! Return to ACTfT. Load to conveyor

6 7 8 9 10 11 12 13 lk 15 16 IT 20 21 Elapsed time - minutes •

Reference 11 26

3.3 CHANGES IN THE SYSTEM

Considerable alterations are being made in the baggage system at Vancouver Airport . Three more carousels and an associated conveyor system are being added to enlarge the domestic baggage claim area (see FIGURE 7 ) since it is felt that the present system is not adeguate to cope with the volume of passengers projected to 1980.

This new area is expected to open in September 1977.

It is hoped to use the model in this thesis to help predict the behaviour of the new system during peak periods.

Several hypothetical changes are made in the model to investigate the sensitivity of the system to certain variables;

1) The number of men unloading bags at

the different points can be varied by altering

unloading times to estimate the sensitivity of

labour on the movements of the bags between the

plane and conveyor.

2) Conveyor speeds may be altered up to

a certain maximum which the MINISTRY OF TRANSPORT

has limited to 100 ft/min. Above this speed bags

begin to jam at corners or at the carousel.

3) Traffic densities may be altered.

That is, during peak periods several planes may be

scheduled very close together. Bags * arrive from FIGURE 7 gates Map of Carousel Area

Planned additional Present domestic unloading conveyors area

PassengersS. arrive Passengers from gates arrive from gates

Present Baggage Claim Area (domestic) ® © 4 /

ro Ground Floor Arrivals Level

L, Entrance) car rentals [ Entrance ( New Entrance 28

3.4 MODEL REQUIREMENTS

The model will begin with the arrival of an airplane at time t=1. Different sizes of airplane are possible.

The model will provide measures of airport level-of- service in terms of queue lengths and passenger waiting times.

The model is also designed to possess multi-stage development capability. A small amount of additional development would be needed to link it to models of other areas of the airport. For example, as is explained in Appendix C, with the addition of more data on probability distributions of processing times through customs, the international arrivals module could be easily added to the domestic module. The output from this module could also be used as input to a model of that part of the Iandside subsystem from the airport terminal to the airport boundary. 29

3.5 DATA REQUIREMENTS

3.5.1 INPUT DATA

Input data can be classified into three categories: facility data, traffic data and operational data.

The facility data encompasses a layout and dimensional description of the Iandside complex. TABLE II presents the types of facility data reguired.

TABLE II

FACILITY INPUT DATA

CORRIDOR length

CONVEYORS length, speed

CAROUSELS capacity, speed (bags cannot be fed onto

the carousel at a speed faster

than that of the rotating carousel)

BAGGAGE TRUCKS capacity,speed

The traffic input data reguired is presented in TABLE

III on the next page. 30

TABLE III

TRAFFIC INPUT DATA

AIRCRAFT type, capacity

PASSENGERS arriving, connecting

QUANTITY OF BAGGAGE arriving, connecting

BAGGAGE PER PASSENGER

The input operational data is a quantitative description of how the traffic and airport facilities behave or operate. TABLE IV presents a list.

TABLE IV

OPERATIONAL INPUT DATA

EMPLOYEE WORK SCHEDULE -specifically, number of

& ALLOCATION OF employees on hand to

EMPLOYEES TO handle baggage.

WORK AREAS -number of employees at

positive checkpoint

FACILITY SERVICE TIMES -rate of flow of

passengers leaving aircraft.

-rate of flow of unloading bags 31

PASSENGER -average walking speed to baggage

area

BAGGAGE TRUCKS -time between aircraft and

conveyor. 3.5.2 OUTPUT DATA

The simulation model outputs three basic types of information: i) passenger and baggage flow data ii) passenger and baggage count {occupancy) and queue length.

Iii) passenger and baggage processing time and

delay distribution

The flow data is basically a count of the traffic passing a specific point for some time interval. TABLE V illustrates the flow outputs of interest. The periods of peak traffic flow are of particular interest.

TABLE V

TRAFFIC FLOW REGIONS OF INTEREST

PASSENGERS LEAVING BAGGAGE CLAIM AREA PER 10 MINUTES

1/2 HOUR

" ii ., II HOUR

HATE AT WHICH BAGS AERIVE AT CAROUSEL PER 10 MINUTES

1/2 HOUR

HOUR

The count data is the instantaneous occupancy of certain facilities generally categorized as holding rooms.

TABLE VI presents a list of occupancy data to be collected. 33

TABLE VI

OCCUPANCY MEASURES

BAGGAGE CLAIM AREA queue lengths, number

of passengers, number of bags,

number of carousels in use.

The passenger and baggage processing time and delay distributions arise from aggregating all of the processing or delay times on a per flight basis. As each passenger or piece of baggage is processed, processing times are calculated and added to the total. 34

3.5.3 METHODS OF DAT1 COLLECTION

The simulation model requires extensive data for

operation and for establishment of validity.

In this case, much of the data was easily obtainable

from the airport authority. The monitoring methods that they

used to obtain traffic and operational data varied among four

common alternatives.

1) traffic counters

2) passenger surveys

3) human observers

4) movies or video tape

The physical distances reguired in the model were

measured off airport blueprints.

Personal observations were made of the actual aircraft unloading process to supplement data provided by the airlines.

Refer to appendix A for a description of the data collection. 35

3.6 FORMULATION OF TIME ORIENT ED MATHEMATICAL MODEL

Most simulation models reproduce existing situations

either by a) continuous simulation of dynamic equations or by b) generating random operating times which have the same

distribution as the process being simulated. The model in this

study uses the second method. Probability distribution

functions are used to describe arrival patterns and service

processes.

Inputs and outputs of this type of model are shown in

HIGURE 8.

FIGURE 8

Reference 3

TIME-ORIENTED INPUTS QUEUEING OUTPUTS MODEL ARRIVAL DISTRIBUTION DISTRIBUTION OF MEAN ARRIVAL RATE WAITING TIMES SERVICE TIME DISTRIBUTION PASSENGER ARRIVAL AND OUEUEIN'G DISCIPLINE DEPARTURE TIMES RENEGING FREQUENCY ARRIVAL AND DEPARTURE NUMBER OF FACILITIES FLOW RATES MEAN SERVICE RATE QUEUE LENGTHS

Figure 8 Typical Inputs and Outputs of a Time Oriented Queueing Model

The input arrival distribution is described in terms

of the interval between successive arrivals. These vary

stochastically, reguiring a probability density function, which

forms the basis for random number generation, for computing the 36

times of arrival at the service facility. The mean arrival rate, which may be fixed or a function of time

Service facility time distributions are also specified by a probability density function and require a mean service rate to generate the random service times. 37

3.7 FORMULATION OF COMPUTER MODEL

The model is formulated as a module that could eventually be connected if desired into a series of program modules where the output of one module is the input for another module.

Each line connecting blocks in the time/distance diagram (FIGURE 9) represents the distance between each activity and, depending on the passenger walking speed distribution, a passenger transit time distribution can be computed. Depending on unloading rates, a bag transit distribution can also be formed.

The arrival of the passenger bags is the determining waiting parameter for most deplaning passengers, flag arrival is computed based on

1) the average unloading time per bag

2) the capacity of bag cart

3) the distribution times required to move bags from aircraft

to conveyor

4) the time required to transfer bags from cart to

bag claim facility. ,

See FIGURE 9 for an illustrative time flow analysis with sample numbers. The paths of passengers and bags are followed to the airport boundary. The numbers in FIGURE 9 represent a hypothetical plane arrival with hypothetical probability distributions throughout the process. FIGURE 9 helps emphasize the overlapping and interdependent relationships in the deplaning process. For example, the first passenger arrives at 38

the baggage claim between 6:32 and 6:33. By the time he leaves the terminal exit the spread of possible times lies between 6:34 and 6:39. When he leaves the parking exit the spread of possible times has risen to between 6:37 and 6:45. Clearly with the passage of time, the expected arrival of the first passenger at any particular point lies in a larger and larger time interval.

a simulation model of the parking lot and supporting ground transportation infrastructure could use as part of its input the output of this model, namely the passenger flows out of the baggage claim area. 39

FIGURE ^

Aircraft Opens Door at 6:30 Aircraft 2.5 sec/bag unloading Passengers Unload at Average time

Rate of Every 3 Seconds 140 hags 6Q bags/cart 20-30 Passengers 3 carts sec cart transit transit GATE time time 70+5 sec. First Passenger 120-180 sec Arrive Between Transit time 6:32:23 and 6:33:33

RENTAL CAR BAGGAGE COUNTER CLAIM Cart transfer Passenger rate-* 1.5 bags/sec Search Time .5-90 sec 30-40 sees transit time

TERMINAL Bags arrive at First Passenger Arrives at EXIT baggage claim the exit between 6:3^:11 between 6:33:36 and 6:39:50 and 6:37:35

120-180 sees •jrtransit time First PRIVATE RENTAL TAXI/BUS/LIMO Passenger CAR CAR BOARDING Arrives I PARKING PARKING AREAS Between 6:36:31 j and 6:42:50 ' 60-120 sees PARKING First Passenger delay EXIT leaves parking lot between 6:37:11 and 6:44:35

30-40 sec transit time First Passenger Leaves Airport Between Airport 6:37:41 and 6:45:35 Exit

Reference 3

Figure 9 Illustrative Time Flow Analysis 40

3.7.1 SIMULATION LANGUAGE

The operation modeled is essentially a multistage gueueing process. GPSS { General Purpose Simulation Language) was chosen as the most suitable and easiest language to use because of its features for handling gueueing operations, particularly for providing detailed gueue statistics. Also GPSS is well supported at UBC.

The reader may refer to the appendix for a listing of the GPSS program used here to model the deplaning domestic system and for the complete flowchart.

Illustration 3 provides a capsule flowchart of the model.

The core of the model contains five basic sections.

The first section is the Originating Flight Section through which passenger transactions move. The user inserts the time of plane arrival and assigns different parameters such as number of passengers and gate number. The carousel is assigned in this section also. Before leaving this section the bag transactions are created., The passengers go to the Passenger Processing

Section and the bags go to the Baggage Processing Section .

In the Passenger Section passengers leave the plane according to the distribution LVPLN and walk to the carousel with walking speed and distance calculated by V$8ALK . After matching, passengers leave the model through the positive claim checkpoint.

In the Baggage Section bags are first rearranged in random order on a user chain reflecting the mixing that occurs 41

in loading and unloading. Then the Equipment Pool Section is triggered and trucks (number supplied in P12 ) start to fill up with bags at a rate described by 0FF1D . Ihen the capacity of the truck is reached ( P3 of truck transaction) the truck drives to the conveyor and unloads.

For 747's and 1-101l's a different block sequence is followed. OLD'S are unloaded following the process described by

Illustration 2 ., Eguipment is positioned within a certain time interval and the OLD'S are off loaded two by two from each hatch until all bags are off loaded., As soon as OLD'S are on the dolly they are taken to the baggage conveyor area. The path of

747 bags then joins the path of regular bags. The bags pass through a MATCH block conjugate to the passenger MATCH block and after waiting and transit times are tabulated the bags leave the model. 42

ILLUSTRATION 3

Capsule Flowchart

Originating Flight Section Equipment Pool Section

GENERATE - create passenger GENERATE create pool of transactions , baggage trucks for CP and AC ASSIGN P1, P2, P3, P4 and P12 are ASSIGN P3 capacity of truck user supplied TEST wait until a flight -*\— ASSIGN P5 carousel comes in

•ASSIGN P6, P7, P8 ENTER BUSY enter busy status

SPLIT P6 create bag TEST is it a 747 or L-1011? transactions ADVANCE FN$OFFLD unload bag

Passenger Processing Section UNLINK SEQ1 remove bag from user chain ADVANCE FN$LVPLN leave plane

LOOP P3 loop back until cart full TEST E P6,2,0UT connection passengers leave model ADVANCE drive to conveyor area unload and test if plane TEST E P7,0,0UT passengers is empty otherwise return without bags to pool leave model

ADVANCE V$WALK walk to assigned Baggage Processing Section carousel

MATCH wait for bags ASSIGN P10 .rearrange bags in random order

ENTER CLCHK enter positive LINK put bags on user chain claim

ADVANCE FN$BLAY check tags SPLIT P12 create number of trucks required

LEAVE CLCHK leave positive GATE trigger equipment claim pool

TABULATE waiting and SEQ1 GATE wait for full truck transit time SEQ2ADVANCE drive to conveyor area TERMINATE and unload

747 & L-1011 Baggage Handling ADVANCE move on conveyor to carousel

SPLIT 2,LATER use two hatches MATCH wait for matching

LINK P4.10 TABULATE waiting and transit times LATER ADVANCE unload each pair of ULD's TERMINATE UNLINK P4,SEQ2,K100,,, NONE remove bags from plane ir groups of 100 TRANSFER ,LATER

NONE TERMINATE 43

3.7.2 SCOPE OF THE MODEL

fi key problem in any simulation and one on which its success or failure may hinge is the setting of the model's scope. The real situation must be structured into a model that provides an accurate representation of the physical and operational system.. k decision must be made on how much detail to incorporate in the model. Also, in the case of a large system like an airport, with many interlinking processes and interfaces as explained in Section 2.1, a decision must be made on where to begin and end the model.

It was decided that the simulation model here should begin with an aircraft arriving at a gate and end with the exiting of passengers with their baggage into the building lobby. Thus the model studies a microcosm of the overall airport system, an approach recommended by Mellichamp*. 44

3.7.3 MODEL LOGIC

Data for the model is structured in two ways:

1) certain times and distances are constants. For

example, walking distances from each gate to each

baggage carousel are contained in a matrix.

2) passenger characteristics and processing rates are

stored in cumulative density functions {CDF). For

example, the number of bags per passenger depends

on whether the passenger is on business or on

vacation. The appropriate probability

distribution of bags is then selected.

TABLE VII shows a list of the data contained in the model structure along with its source and exactly how it is written into the model.

TABLE VII

SUMMARY TABLE OF DATA CONTAINED IN MODEL

CONSTANTS

DATA FORM IN MODEL SOURCE- * Distances from Matrix savevalues Measured off airport carousels to gates "DIST" blueprints distances from gates matrix savevalues measured off airport to conveyor area by "DFGTC" blueprints cart conveyor lengths to matrix savevalues measured off airport carousels "CLGTH" blueprints 45

DATA EOBfl IN MODEL SOUfiCE

capacity of OLD each OLD will hold observation 3500 pounds or about (appendix) 45-55 bags. An average of 50 bags/ OLD is assigned. capacity of baggage Capacity of storages measured off airport carousel 61-67 calculated blueprints from dimensions of carousel assuming 2 sg.ft per bag on avg.

number of personnel Capacity of storage user supplied at positive claim CLCHK check

plane type parameter 1 user supplied

number of passengers parameter 3 user supplied in plane

airline parameter 2 user supplied

gate number parameter 4 user supplied

number of trucks parameter 12 user supplied available at airplane gate

time of arrival of C operand of user supplied plane generate block 46

CUMULATIVE DENSITY FUNCTIONS

DATA FORM IN MODEL SOURCE

% arriving or % function AB'HCT user supplied connecting passengers

% business or % function PLEAS user supplied vacation passengers

number of bags per function BAG depends user supplied passenger on business or pleasure rate of passengers function LVPLN data supplied from leaving plane M.O.T. Study (appendix)

passenger walking function VEL data supplied from speed M.O.T. Study: 29 passengers/minute per door and plane types 1»2,3 (747, 1011, DC-10 ) have 2 doors for unloading.

time to remove a bag function DLAY varies Rogers Lui7. from carousel when uniformly between 3 both passenger and and 50 sec. {the bag are present time of 1 revolution of carousel). rate of Rates of 7.5 and 6 Air Canada Handbook ioadi ng/unloading sec/bag were of Standards bags maintained using function OFFLD S CONLD service time to function BLAY observation match tags at claim samples a discrete (appendix A) check distribution giving on average 4.5 seconds /bag capacity of baggage function CAPCY Air Canada Handbook cart varies uniformly of Standards from 80-90 bags 47

The passenger and baggage flow through the deplaning domestic module is represented in a flowchart in FIGURE 6 . ,

At first the only transactions in the model are passenger transactions. Each passenger transaction enters the model through a GENERATE block so each is in a different assembly set. When the carousel is assigned, a SPLIT block is used to create bag transactions. Each transaction has the following 12 parameters:

P1 = plane type 1=747, 2=L-1011, 3=DC-10, 4=OTHER

P2 = airline 2= CP AIR 1= AIR CANADA

P3 = # of passengers in plane group

P4 = gate number

P5 = carousel assigned

P6 = 1=arriving, 2=connecting

P7 = # of bags/passenger

P8 = 1=business, 2=vacation

P9 = bag waiting time per passenger

P10 = random number assigned to baggage

P11 = bag waiting time at carousel (maximum time

until all bags of 1 passenger arrive and are

matched with passenger)

P12 •= # of baggage trucks to meet plane

Parameters 1,2,3,4,12 are input to the model at the user's command and serve to identify it immediately after the transaction is generated. Parameter P5 , carousel assigned, and the parameters giving waiting times, P9 and P11 are assigned by by the model logic. P6, P7, P8 and P1G are assigned using probability distributions. 48

Durinq carousel assignment only certain carousels are

available to each flight accordinq to which airline the flight belongs (with Air Canada or CP AIR in this model). The carousel availability rules accordinq to airline which were used here are illustrated in FIGURE 10. These rules may be altered by the

user if so desired.

FIGURE 10

Present System

Figure 10 Carousel Availability The logic for carousel assignments is based on considering two types of aircraft: 747*s and other types, A basic set of rules were developed as follows:

1) carousels are assigned to flights in the order

of their arrival. ,

2) only certain carousels are available to each

airline ( FIGURE 10)

3) after one flight has been assigned to each

carousel, the next flight is assigned to the

carousel with the fewest bags in its system. Here

system means bags in the carousel, bags in cart or

OLD storage, and bags still sitting in a plane

destined for that carousel.

4) both halves of a 747 jet are not assigned to

the same carousel unless

a) the plane had less than 250 passengers, and

b) the carousels are already so congested that

it makes more sense to put both halves of the 74 7

on the same carousel i.e. the second half of the

plane would be serviced more quickly if both

halves were assigned to the same carousel.

The above rules are the same as those used at

Vancouver Airport .

Bag transactions are generated in the same sequence as passenger transactions but are then mixed randomly. A random number is assigned to parameter 10 of each bag transaction and 50

the bags are then ordered according to this random number

seguence (numbers from 0-999).

For all planes, bags of a given flight are delivered

to the baggage conveyor leading to the carousel in a number of

truckloads or in groups of ULD'S in the case of 747»s.. For each

plane a certain number of baggage trucks are assigned according

to the user supplied Parameter 12. Each airline has a pool of

trucks. CP AIR has 10 trucks and air Canada has 17 tractors

each of which can tow up to four tubs. This is different from

the CP AIS trucks which carry the bags on the truck itself. In

this model it is assumed that the Air Canada tractors tow four

tubs. This is the usual number. The capacity of four tubs is

140 bags plus or minus 10 bags. Not until at least one truck is

available can unloading begin. After the full truck drives to

the conveyor and the bags are unloaded, the model checks if the

plane is completely unloaded. If so, the truck returns to the

pool of trucks of the respective airline., If not, the truck

returns to the plane to complete the unloading.

For 747*s the DID'S are unloaded directly from the

plane onto dollies and then driven to the conveyor where they

are offloaded., For 747*s , trucks are used for 15-30 minutes

per flight (this variable is randomly selected).

The baggage arriving at the assigned conveyor will be

loaded onto the conveyor. If another truck is still in the

process of unloading the arriving truck waits until it can

unload. The rate at which bags are loaded onto the conveyor is

limited by the speed of the conveyor (100 ft/min or about 15

bags/min assuming an average of 7 feet of space per bag). The 51

baggage employees load bags at a certain normal rate. This rate

is recorded in the Air Canada Handbook of Standards.

Passenger transactions arriving at the assigned baggage carousel are reunited with their baggage through the use

of a MATCH block. When all of a passenger*s bags have arrived at the carousel he is permitted to leave the MATCH block., The baggage claim area around the carousel was the most difficult

part of the process to model. Such factors as the number of

passengers able to search simultaneously for their bags must be considered. According to the paper by Sogers Lui etal, motion picture analysis of passenger activity around a carousel

indicates that almost all passengers remove their bags during the first cycle for which both the passenger and the bags are in

the baggage claim area. Hence, considering the time a bag takes

to complete a full circle on the carousel a CDF , Cumulative

Density Function , is used to sample a value between 3 seconds

and the time of 1 revolution -50 seconds- before the bag is

removed by a passenger.

After leaving the baggage carousel area the passenger

checks his bags through the positive claim checkpoint. A

discrete empirical function was formed to describe the time

required at this checkpoint. Befer to appendix A for a

description of this distribution. It was found that the number

of bags a passenger had scarcely affected the tag check time;

the time depended on the individual {Whether the tag was ready

for presentation or whether it was in the inside right pocket

were the main variables, not number of bags).

The following points summarize the logical assumptions 52

made in the model.

1) hags and passengers are randomly mixed. No

preference is given to the bags of first class

passengers. This was felt justified because first

class baggage delivery is often uncertain. If a

group of economy passengers arrives late, their

bags may be packed last instead of the first class

bags. Ignoring first class was also felt

justified because airlines are slowly phasing out

first class on most domestic flights.

2) for assumptions in carousel assignment see page

50.

3} it is assumed that passengers retrieve their

bag from the carousel within one revolution of the

carousel from the time when both bag and passenger

are present at the carousel. The time of

retrieval between a minimum of 3 seconds and a

maximum of 50 seconds is randomly assigned using a

CDF.

4) it is assumed that the baggage employees

maintain an unloading rate of a certain number of

bags per minute. This was measured several times

by direct observation of the employees and also

figures were obtained from the Air Canada Handbook

of Standards. Direct observation verified the 53

Handbook rate of .25 man-minutes per bag at the

plane and .1 man-minutes per bag at the conveyor.

5) it is assumed that the pool of equipment

available to each airline is fixed. However

limitations can be introduced to the model by

altering the number of trucks in each airlines

pool and the truck capacities if desired.

6) as soon as 01D»S are unloaded from the plane

and placed on dollies they are driven to the

conveyor. It is valid to assume a tractor will be

there to transport the containers since the time

to offload is greater than the time of a return

trip to the conveyor.

Clearly many of the processes in this model are subject to the human element. For example, if a passenger with a wheelchair is on the plane, passenger deplaning may be slowed

down. There is a point in any model in which the limit of

detail to be included must be decided. It was felt that the

model in its present form and under the present assumptions

adequately represented reality. The comparison to real situations is discussed in Section 4.1, Hodel Validation. 54

3.7.4 THE PROGRAM

For a complete flowchart of the GPSS program and for a listing of the program, the reader may refer to Appendices D and

E for greater ease of reference. Program dimensions were as follows:

160 blocks

4500 maximum number of transactions in the system at one

time

Other limits can be seen on the

REALLOCATE statements. About 2 minutes of central

processing time is required to simulate a four

hour peak period in which up to approximately 5000

transactions {passengers and bags) are processed.

FlGORE 11 shows the cost to run the program as a function of the number of transactions (passengers and bags) processed. The cost is about $10 per 1000 transactions. Thus it is possible to model two full 747's, for example, for about

$12. ,

56

4 SIMULATION RESULTS

4. 1 VALIDATION

Validating the computer model was done by performing comparisons between observed and computed quantities.

The specific figures compared were for a specific flight:

1) time of arrival at baggage carousel of first

passenger and cf last passenger.,

2) time of arrival at baggage carousel of first

bag and last bag.

3) time of first match and last match of passenger

and bag.

For this validation the following figures were obtained and known for a given flight:

1) the number of passengers on the plane

2) the number of connecting passengers

3) the above mentioned 3 time points.

On the next page is a table of validation results for two CP flights. TABLE VIII

Validation Results of 2 Sample Flights

CP 69 Gate 22 DC-8 CP 63 Gate 23 737

65 passengers 58 passengers Arrival observed simulated observed simulated of: 1st pass. 3.0 3.5 (.485 3.0 3.5 (.511)

last pass. 9.0 8.8 (.406) 8.5 9.3 (.918)

1st bag 9.0 9.0 (.408) 8.5 8.8 (.338) last bag 15.0 14.7 (.961) 12.5 13.3 (.707)

Time of:

1st match 9.5 9.6 (.094) 9.0 9.3 (.918) last match 15.5 16.0 (.408) 13.0 13.6 (.624)

Note: 1) All times arc in minutes 2) Standard deviation is in parenthesis 3) Simulated results are averages over 5 runs 4) All results lie within one standard deviation of observ. 58

The validation results in TABLE VIII show that the mean of the simulated results lies within about one standard deviation of the observed values of the different time points measured. The observed values in TABLE VIII contain a small measurement error., The stopwatch was started when the first passenger stepped through the door of the plane. The next measurements were taken when the first and last passenger reached within 15 feet of the carousel. The last passenger arrival could not be exact to more than two or three seconds due to difficulty in seeing through the crowd.

On the following pages in FIGURES 12 and 13 are graphs showing actual and simulated times of last bag arrival as a function of number of passengers in a DC-9 and also in the 747 and L-1011 (container-handled planes). The DC-9 accounts for

50% of Air Canada's flights. That is why it is studied here.

For each plane type only one gate was considered at a time.

Holding this variable constant allowed easy comparison of passenger and bag transit times. Otherwise, the transit times from different gates can vary within about a minute. The observed points were collected in Hay, 1977.

The simulated results are superimposed on the observed values in FIGURES 12 and 13. The simulated mean value of last bag (for 5 runs) is plotted with crosses at each value for number of passengers. A solid line shows the trend in these results. Dotted curves approximate a 2 standard deviation envelope of simulated last bag values about the simulated mean.

These dotted curves show more clearly how the simulated results compare with actual values. 59

The airlines in this model have policy standards on last bag arrival to which references in this thesis will be made. The airlines hope to achieve the following standards 90% of the time:

1) Last 747 bag within 30 minutes of plane arrival.

2) Last L-1011 and DC-8S bag within 25 minutes.

3) Last DC-8 and 727 bag within 20 minutes.

4) Last DC-9 bag within 15 minutes.

The observed last bag times show more scatter than the simulated runs show., If more simulated runs had been made more scatter points would have resulted. In the DC-9 case ( FIGURE

12) a trend is clearly visible in both the simulated and observed points. This emphasizes the increase in last bag time as a function of passenger load. The simulated run suggests that with a passenger load of more than 70 or 80 passengers, it will be difficult to meet the 15 minute standard for last bag.

In the graph for the larger planes, the 747 and L-

1011, FIGUBE 13, the observed points do not display nearly as clear a trend as was found for the DC-9 . There are several times where the last bag on a 7 47 is delivered later than the 30 minute standard. The simulated results suggest that for more than about 140 passengers on a L-1011, there will be difficulty in meeting the 25 minute standard for last bag. This suggestion is supported by the beginning presence of observed points over the 25 minute mark at the 140 passenger level. For the 7 47, the simulated results suggest that for more than 340 passengers it will be difficult to meet the 30 minute standard of last bag.

This is supported by actual observations. above the 300 60

passenger level there are fewer instances of meeting the 30 minute deadline.

Note on FIGURE 13 that there is a distinct break in the simulated results at the 200 passenger level. This represents the passenger volume at which two carousels are allocated to a 747. ,. The L-1011 and 747 are unloaded in the same patterns as shown in Illustration 2. The L-1011 is always allocated one carousel. Below 200 passengers, the 7 47 is allocated only one carousel also.

63

The following three figures which show graphically some different waiting times are included in the Validation

Section to help illustrate the matching process and to show that it is modeled correctly. These particular figures were taken from a simulation run of a 747 with 340 passengers at gate 10.

FIGUBE 14 shows the time passengers must wait for all their bags to arrive at the carousel.,, From this graph it can be seen for example that after 14 minutes 50% of passengers have received all their bags. After 19.5 minutes , 90% of passengers have received all their bags. Also it can be seen that the minimum waiting time of a passenger for his bags was 6.5 minutes. This is compatible with actual data which shows that all passengers have arrived at the baggage claim area from the gate within 11 or 12 minutes after the plane door opens. Actual data shows that for 340 passengers the last bag commonly arrives in around 24 to 25 minutes. Thus the above simulated matching rate seems reasonable.

FIGUBE 15 shows the time a bag must wait until all bags of that passenger arrive. Thus this graph is similar to

FIGUBE 14 except it is from the bag*s point of view. This graph shows the effect of the random order of bags on passenger waiting time. From this graph one can form an idea of the time between bags of one passenger. It can be seen that 55% of all bags wait only 30 seconds or less. This will mostly be with passengers with only one bag. These times compare favorably with . actual data. Actual data indicates that 5555 to 58% of passengers usually have only 1 bag (this data is contained in the BAG function) and 95% to 98% have 2 or less bags. In FIGUBE 64

15, after 7.5 minutes of waiting, 90% of the hags have been matched and taken away. The greatest spacing between the first and last bag of a passenger was 11.5 minutes., Actual data shows that for 340 passengers the spread between first and last bag will be 15 minutes on average (assuming .1 man-minutes unloading time per bag).

FIGURE 16 illustrates the combination of passengers arriving, bags arriving and the matching of passengers with their bags. For example, 100% of passengers arrive by the 11 minute elapsed time mark. Since the first bags arrive at 11.5 minutes, the first passengers then begin to retrieve their bags and leave the carousel area., The process continues until the last bag arrives at 25 minutes and the last passenger leaves with the last bag at 25.5 minutes. This compares accurately with actual data which indicates that the first match occurs within a minute of the first bag arriving and the last match occurs within a minute of the last bag arriving. FIGURE , 14

Simulated 100 — Waiting Time for Passenger to Receive All Bags • • • in Carousel Area • • • • Af tejc 13^ 5—mi-mitea— 90% of passengers TT have received all their bags

. After 14_m:Lniite_s _ 50% of passengers have received all their bags

Minimum waiting time of a passenger for his • 'brag's ^a~5 ~p.~!b ^m^tes-fr'

10 15 20 WAITING TIME (MINUTES) 66

"PIGURU 15 Simulated -Waiting Time Por All Bags of a Passenger to Arrive 10(H • o • • e o o f* greatest spacing between 90/Q of bags_are_ raatched_0' 90 1st and last and tafcen away within a , 7.5 minutes of bag of a arrival of 1st passenger was 12 80 bag of a passenger e o a minutes 0 • 70

ft60

L-O

O 50 CD 55/6 of bags are to cd matched and taken +J away within 30 g4C[ seconds of their u arrival CD P4

•H +> o !20

10 I

5 10 15 WAITING TIME (MINUTES) FIGURE 16' Simulated Passenger Transit Time and Bag Transit Time

and Matching Process

m m f m XX * I Example: 1 last Joe1s 2nd . bag bag arrives I arrives

all passengers arrive B Bag Transit Time by 11 Passenger minutes Transit Time

Sum of Passenger Bag Wait Time and Example: Transit Time Joe arrives Example: Joe's 1st bag arrives

Example:| Joe has retrieved all his bags by this last point and leaves I passenger |leaves with bag(s) 10 15 20 25 30 TIME (MINUTES) —4 68

4.2 SIMULATION EXPERIMENTS

In this section, the different experiments and situations run with the model are described.

Experiment 1 ) The sensitivity of the system to changes in baggage conveyor speed was tested. Presently the system runs at the maximum recommended speed of 10 0 ft/minute. If wider and more modern conveyor belts were installed it is conceivable that speeds of 150 and 200 ft/minute might be attained. Simulation runs on a typical flight were made using these different conveyor speeds. ,

Experiment 2 ) The rate of unloading bags was another variable changed. The rate of unloading bags from a plane onto the trucks and from the trucks onto the conveyor depends on the number of employees working. The present unloading rate observed was 1 bag every 7.5 seconds on average at the plane and

1 bag every 6 seconds at the conveyor based on the data in the appendix. The sensitivity of a typical flight to unloading rates of 1 bag every 2,4,6,8,10 and 12 seconds was tested.

Experiment 3 ) The number of people helping to check baggage tags at the positive claim point was varied for different passenger levels and the gueue statistics were noted.

Experiment 4 ) The first three experiments were analysed by compiling statistics on one typical flight. The fourth 69

experiment involves holding the first three variables {conveyor speed, unloading rate and checkout personnel) to their present values and altering the traffic flow through the system. That is, with different arrival schedules, the gueue statistics are observed. This experiment takes place in three stages;

4a) using the present system, two 747*s were

scheduled for CE AIR . It was observed how

closely they can arrive while maintaining the 3 0

minute standard for 747*s {the last bag arriving

within 30 minutes of the plane reaching the gate) .

CP AIR has the use of two carousels for their

747's provided the second one is free and not used

by Air Canada .

4b) the same was done for Air Canada under the

present system in which they use 2 carousels plus

1 racetrack.

4c) under the new carousel system, CP will use

three carousels which is a situation similar to

Air Canada's present operation. However Air

Canada will have four carousels. Two 747*s were

scheduled simultaneously. This occupies all four

carousels. A DC-9 was then scheduled at varying

subseguent intervals. The time of first and last

bag of both the DC-9 and the 747 which it follows

was measured. For the above three sections of 70

experiment four, results were plotted for different passenger levels in the 747»s. Runs with 240, 340 and 440 passengers in the 747's were made. These variations represent different levels of congestion. The DC-9, however, had 10 0 passengers each time.

After these simulation runs the following calculations were made. Known data on the distribution of actualplane arrivals about the scheduled arrival time was obtained for the month of May. , The scheduled interval between planes was then calculated by establishing a 95% confidence interval. First, the actual interval was determined graphically (experiments 4a-

4c)., Secondly, this actual interval was expanded into a larger scheduled interval to assure that 95% of the time the actual interval did occur. 71

4.3 ANALYSIS AND RESULTS OF SIMULATION

Figures summarizing the results of the experiments are presented on the following pages. , In experiment 1/ 3 runs using different random number seguences were made for each conveyor speed. The mean of the 3 runs was plotted. In experiment 2, two runs were made at each unloading rate and each run vas plotted. In experiment 3, each plotted point represents 1 simulation run. In experiment 4, each plotted point represents the mean of 5 runs. More runs would provide more accuracy, but time was a limiting factor.

Experiment 1 is summarized in FIGURE 17. For conveyor speeds of 100, 150 and 200 feet/minute, the first and last bag times of a 747 with 340 passengers at gate 10 were observed.

The result is that the mean bag arrival times decrease with increased conveyor speed. This result is plotted in FIGURE 17 to show clearly this effect on bag delivery.

The first bag time, at 150 feet/minute, decreases 30 seconds from the delivery time at a conveyor speed of 100 feet/minute. The gain is a full minute with a 200 feet/minute conveyor speed.

The last bag time, at 150 feet/minute, decreases 2.5 minutes from the delivery time at a conveyor speed of 100 feet/minute., A further 30 seconds is gained with the increase to 200 feet/minute. The increase is not linear for last bag arrival because the rate of unloading onto the conveyor ultimately limits the time of last bag. FIGURE 1-7* Varying Conveyor Speeds

-a

Last Bag

(340 passengers 747 at gate 10)

- First Bag

• t • t • i 1 i i i 1 1 1 1 100 150 200 CONVEYOR SPEED (FEET/MINUTE) 73

Experiment 2 is summarized in FIGURES 18 and 19. For the DC-9, varying the unloading rates affects both first and last bag arrival times because the bags are unloaded

individually both from the cart to the conveyor and from the

plane to the cart. The relation of last bag time to unloading

rate is linear.

FIGURE 19 presents the same results for the 747. The

first bag time is independent of loading rate since the bags are

unloaded only at one point. The last bag time is related linearly to unloading rate except at below 4 seconds/bag., Since

the present unloading rate is 6 seconds/bag it is clear that

there is not much room for a guicker unloading rate before the

capacity of the conveyor is a limiting factor. Two men

unloading at 4 seconds/bag is the limit. Or since one man

normally unloads at a rate of about 6 seconds/bag three men

unloading at a rate of 6 seconds/bag reaches the limit on

conveyor capacity. FIGURE re Varying Unloading Kates of Bags »

Last Bag

First Bag

(DC-9 80 passengers)

J L _J I I I l_ 2 4 6 8 10 12 BAG UNLOADING RATE (SECONDS/BAG)

FIGURE 1?_ Varying Unloading Rates of Bags

(747 - 340 passengers at gate 10)

First Bag

2 4 6 8 10 12 BAG UNLOADING RATE (SECONDS/BAG) 75

FIGURE 20 presents the results of experiment 3 which explored the average passenger waiting time at the positive check point for different passenger levels. The situation for 2 employees at the check point is adequate (average wait less than

30 seconds) until the 500 passenger level. That is, when a flight (or a combination of flights arriving close together) places 500 people in the claim area waiting for their bags, the resultant flow from the baggage claim device will cause an average wait of 40 or 50 seconds per passenger. Above the 500 passenger level, the average waiting time rises very steeply.

With 3 employees at the check point, the average wait at the 500 passenger level is reduced to between 5 and 10 seconds. In this situation, with 3 employees, the average waiting time in the tag check gueue reaches 40 or 50 seconds in the region between 900 and 1000 passengers. Above the 900 passenger level, the average waiting time again rises very steeply. 76

FIGURE 20

NUMBER OP PASSENGERS IN BAGGAGE CLAIM AREA 77

The results of experiment 4, presented in FIGURES 21-

32, determine the recommended intervals between plane arrivals

under different passenger loads in order to ensure that the last bag arrives within the acceptable standards (described on page

59). FIGURE 21 illustrates the interference between two CP

747's scheduled at varying intervals on carousels 3 and 4.

The purpose of the graph is to show the time interval

between 747 arrivals at which the arrival time of the last bag

falls to an acceptable minimum value (at least below the 30

minute last bag standard). In the case of 747's with 340

passengers, the arrival time of last bag does not come down to

30 minutes until the 15 minute interval mark. at this point the

first bag of the second 747 arrives after the last bag of the

first 747. At the 5 minute interval for example the first bag

of the second 747 arrives at 19.5 minutes well before the last

bag of the first 747. The normal last bag time of the first 747

at 29 minutes is pushed back to an unacceptable 42 minutes.

This assumes the planes each have 340 passengers and that the

second carousel belonging to Air Canada can be used by CP AIR at

this time.

FIGURES 22 and 23 show the interference between two CP

747's on carousels 3 and 4 at passenger loads of 240 and 440

respectively. ffith 240 passengers the arrival time of the last

bag of the second 747 falls below 30 minutes at the interval of

10 minutes. With 440 passengers the arrival time of the last

bag of the second 747 falls to a minimum value at the 20 minute

interval mark. 78

FIGURE 21 Bag Delivery for 2 747's Arriving (340 passengers) at varying Time Intervals For CP AIR on Present Carousel System

TIME ELAPSED UNTIL 2nd 747 ARRIVES (MINUTES) FIGURE 22 Bag Delivery for two 747's (240 passengers) Arriving at Varying Time Intervals

TIME ELAPSED UNTIL 2nd 747 ARRIVES (MINUTES) 80

50 ^ FIGURE 23 Bag Delivery for two 747's (440 passengers) Arriving at Varying Time Intervals For CP AIR on present Carousel System

Last Bag of 2nd 747

40

Last Bag of 1st 747

20i^ First Bag of 2nd 747

- -Xr

10

First Bag of 1st 747

10 15 20 TIME ELAPSED UNTIL 2nd 747 ARRIVES (MINUTES) 81

FIGURE 24 shows the situation in which two Air Canada

747's with 340 passengers arrive at different intervals and

utilize Air Canada's 2 carousels and 1 racetrack. From FIGURE

24 it can be seen for a passenger load of 340 that the actual interval between 747*s should be 12.5 minutes. At this interval between flights, the arrival of the last bag drops to an acceptable 29.5 minutes.

Similarly FIGURES 25 and 26 show the interference

between two Air Canada 747's at passenger loads of 240 and 440

respectively. With 240 passengers, the arrival time of the last

bag drops to an acceptable 29 minutes at the 5.5 minute interval

mark. With 440 passengers, the arrival time of the last bag

drops to a minimum level at the 17.5 minute interval mark. 82

FIGURE 24 Bag Delivery for two 747's (340 passengers) w Arriving at Varying Time Intervals U For Air Canada on Present Carousel System S2i M 5 40 - co

M

IS

TIME ELAPSED UNTIL 2nd 747 ARRIVES (MINUTES) 83

FIGURE 25 Bag Delivery for two 747's (240 passengers) CO w Arriving at Varying Time Intervals EH For Air Canada on Present Carousel System 40 MI N ^—* co

M EH >H

H p1-q w 30 CJ <=

20 NT ] S3 r— r— 'd

CaM FirstJ3ag_of Jnd 747 EH X - - O KH X- >M PtJ Last Bag of 1st 747 10 «: s o PH

M EH ?irst Bag of 1st 747

0 5 10 15 TIME ELAPSED UNTIL 2nd-747 ARRIVES (MINUTES) 84

vFirst Bag of 1st 747

10 15 20 TIME ELAPSED UNTIL 2nd 747 ARRIVES (MINUTES) 85

FIGURES 27-29 present situations that may occur on the future carousel system.

FIGURE 27 shows the simultaneous arrival of two Air

Canada 747»s followed by a DC-9 at different intervals. At the

5, 10 and 12.5 minute interval points, the time of first bag delivery of the 747 is held back by the last bags of the 747.

It is not until the 22.5 minute interval mark that the arrival time of the last bag of the DC-9 with 100 passengers stabilizes.

The last bag is then delivered in 17.5 minutes. Thus the DC-9 should not arrive until 22.5 minutes after the two 747's. This actual interval assumes the 747*s each have a load of 340

passengers.

In FIGURE 28 a similar situation is presented except each 747 has only 240 passengers. In this situation, the

arrival time of the last bag of the DC-9 stabilizes at the 18

minute interval mark.

FIGURE 29 demonstrates the situation in which each 747 has 440 passengers. In this graph, the arrival time of the last bag of the DC-9 stabilizes at the 25.5 minute interval mark.

TABLE IX summarizes the calculated scheduled intervals. The scheduled intervals were determined (as described in Section 4.2) to assure that the actual above established intervals occur 95% of the time.

FIGURES 30 and 31 present graphically the relationship between passenger load and the recommended interval between 747 flights. These graphs portray the present situation for Air

Canada and CP AIR.

FIGURE 32 presents graphically the relationship 86

between passenger load and the recommended interval between flights for the future carousel system. 87

FIGURE 27 Bag Delivery for Two 747's (340 passengers)

CO Arriving Simultaneously and for the Arrival w of a DC-9 (100 passengers) at Varying S-I Subsequent Intervals for Air Canada on § 40 the New Carousel System

30 \Last Bag of \ DC-9 \ \

20

10

5 10 15 20 25 TIME ELAPSED UNTIL DC-9 ARRIVES (MINUTES) FIGURE 28 Bag Delivery for two 747's (240 passengers) Arriving Simultaneously and for the Arrival of a DC-9 (100 passengers) At Varying Subsequent Intervals For Air Canada on the New Carousel System FIGURE 29 Bag Delivery for two 747's (440 passengers) Arriving Simultaneously and for the Arrival of a DC-9 (100 passengers) at Varying Subsequent Intervals for.Air Canada on the New Carousel System TABLE IX Scheduled Interarrivals for 95% Confidence Interval

CP AIR AIR CANADA interval (minutes) interval (minutes') Actual Scheduled for ^ Actual Scheduled for * 95% confidence 95% confidence Present two 747's Carousel (240 pass.) 10.0 18.2 5.5 13.7 System 340 " 15.0 23.2 10.0 18.2 AM) ?o.o 28.2 17.5 25.7 Future Carousel two 747's same as Air simultaneous System Canada on (4 carousels) present svstem two 747's • (240 pass.) then a DC-9 17.5 22.1 (100 pass.)

two 747's (340 pass.) - then a DC-9 22.0 26.6 . (100 pass.)

two 747's (440 pass.) 25.0 29.6 then a DC-9 (100 pass.)

* calculated for 95% confidence by + 1.96^ where a sample 747 flight 149 was observed

"~ /n for 27 days giving «r= 10.908 and n=27 and a sample DC-9 flight 237 was observed for 30 days giving 9. 91

FIGURE 30

Simulated Arrival Intervals Between two Air Canada 747's

w20 h on Present Carousel System EH ^Scheduled for 95% confidence

r-

w Actual

•a 10

> w EH

240 340 440 Passenger Load

CO FIGURE ?1 £30 Simulated Arrival Intervals Between two CP AIR 747's on Present Carousel System co

y- Scheduled for 95% confidence S5

EH w 20 pq

>

S5

10 240 340 440 Passenger Load 9 2

FIGURE 31

Simulated Arrival Intervals Between two 747*s and a DC-9 (100 passengers) for Air Canada on the New Carousel System to w £30

"Scheduled for confidence

r^O Actual

i o « 53 w

EH W « 101 w EH

240 340 440 Passenger Load 93

4-4 CONCLUSIONS

This thesis has provided a strong motivation for investigating and developing simulation models for the remaining subsystems of the total air transport world. Both present and future airport development can be considered with simulation.

Terminal buildings are amortized over 20 years. Airport facilities built today should anticipate the accommodation of aircraft twice the size of the Boeing 747.

This study has demonstrated the value of simulation in analysing a segment of the airport through which large numbers of people pass. It provides accurate information needed for the planning and operation of the baggage claim facilities.

Information on the possible density of flight schedules is presented. Also the effect of changing the variables of conveyor speed and of unloading rates was discussed.

The most important step in creating a simulation model is in the validation. Hence much effort in data collection and simulation analysis in this study was directed towards validation.

In summary, this model can be an important operational tool to answer questions such as: What will be the congestion due to certain flights arriving close together? How large a queue will occur with 2 people at the positive claim checkpoint for the arrival of a 747? What is the effect of an additional baggage handler (i.e. A change in unloading rate) on baggage delivery?

Using this simulation model is a guick and inexpensive way to ascertain answers to these guestions. 95

5 'FOOTNOTES .

1. Fortescue, Richard "Baggage Mishandling at Vancouver airport", B.Comm. , Thesis, Faculty of Commerce, DBC, may 1976, p 88.

2. Mountjoy, Kimball "airport Simulation Models" AGIFORS 1969.

3. Klingen, L.G. (Eastern airlines) "A Dynamic Simulation Approach Solves Problems in Facilities Planning and Adds a New Dimension to Conventional Cost Analysis". AGIFORS 1971.., (with accompanying notes from a talk given on this article at the 11th AGIFORS Symposium, 1971).,,

4. Transport Canada, Personal communication with Hr. Ken Krauter, December 1976.

5. Air Canada, personal communication with Mr. B. Baillie, June 17,1977.

6. Mellichamp, Joseph M. And James L. Fillmer, "Simulation and the Superjets". Transportation Journal,- Winter 1973, pp 51-55.

7. Lui, Rogers, Ravinder Nanda, James J. Browne, "International Passenger and Baggage Processing at John F. Kennedy International Airport," IEEE Transactions On System, Man And Cybernetics^ Vol. SMC-2 NO. 2, April 1972, pp~221-225.

8. Robinson, Gerald L. "Simulation Models for Evaluation of Airport Baggage Handling Systems." Battelle Memorial Institute, Columbus, Ohio, 1969.

9. Lowe, Dana E. "Use of Simulation in Airport Planning and Design." Transportation Engineering Journal, November 1974, pp 985-995.

10. Gordon, Geoffrey, System Simulation Prentice-Hall. New Jersey, 1969, pp 118-121.

11* Air Canada Handbook of Standards . published privately for Air Canada use, updated 1976. , 96

6 BIBLIOGRAPHY

Air Canada Handbook of Standards, published privately for Air Canada use, updated 1976.

Gordon, Geoffrey, System Simulation Prentice-Hall, New Jersey, 1969, PP 118-121.

Fortescue, Richard "Baggage Mishandling at Vancouver Airport", B.Comm. Thesis, Faculty of Commerce, UBC, May 1976, p 88.

Klingen, L.G. (Eastern Airlines) "A Dynamic Simulation Approach Solves Problems in Facilities Planning and Adds a New Dimension to Conventional Cost Analysis", AGIFORS 1971. (with accompanying notes from a talk given on this article at the 11th AGIFORS Symposium, 1971).

Lowe, Dana E. "Use of Simulation in Airport Planning and Design." Transportation Engineering Journal. November 1974, PP 985-995.

Lui, Rogers, Ravinder Nanda, James J. Browne, "International Passenger and Baggage Processing at John F. Kennedy International Airport." IEEE Transactions on System. Man And Cybernetics. Vol. SMC-2 No. 2, April 1972, pp 221-225.

Mellichamp, Joseph, M. and James L. Fillmer, "Simulation and the Supersets", Transportation Journal. Winter 1973» PP 51-55.

Mountjoy, Kimball "Airport Simulation Models" AGIFORS 1969.

Robinson, Gerald L. "Simulation Models for Evaluation of Airport Baggage Handling Systems." Battelle Memorial Institute, Columbus, Ohio, I969. 97

APPENDIX A: DISCUSSION OF AIRPORT DATA COLLECTION

The data for the model and for the validation was collected in two ways. Personal observations were made on several trips to the airport and also figures were taken from the Air Canada Handbook of Standards. This handbook is used by the airline to maintain quality standards at each of its offices across the world.

Such values as the maximum speed of the tractors towing baggage carts and the rate of unloading bags have been measured by the airlines and set down in this handbook.,

Air Canada maintains a daily record of the last bag arrival time for each flight. However, to obtain more validation data, personal observations were made as shown in

TABLE VIII. Two people observed the flows for a given flight.

One person met the flight and followed the first person to the carousel, the other person followed the bags down the ramp and observed the process taking place from that angle.

To determine the probability density function BLAY of

processing time at the positive claim checkpoint, data was collected on the time required to match tags of each passenger depending on how many bags were carried. It was found that the number of bags carried was not an important variable. The time to match tags ranged from 1 to 20 seconds with a mean of 4.5 seconds per person over 40 observations. The function BLAY copies the empirical distribution. 98

APPENDIX B: SUGGESTED AREAS OF FURTHER RESEARCH

There are two courses for further research.. Either this model could be expanded to include the deplaning

International Arrivals Model {as explained in Appendix C) or models of other areas of the Airport could be designed.

Examples of models that could be developed easily at

Vancouver Airport are:

1. an Airport Runway Model - to duplicate the arrivals and departures of aircraft at an airport. It could determine runway usage, the extent of ATC delays, congestion at the taxiways and runway crossing problems..

2. a Terminal Airside Model -to simulate the arrivals and departures of aircraft into the terminal. Such problems as gate assignment and eguipment availability would be modeled.

3. an Enplaning Baggage Handling Model -to investigate the benefits of curbside versus ticket counter check-in.,

4. a Ticketcounter Model -to calculate the number of ticketcounters and reguired floorspace in front of the counters.

There are models already in existence for this situation.

5. a Curbside Model -to calculate the reguired amount of curbside for various levels of traffic and determine waiting times and times of vehicles.

6. an Air Cargo Model -to model the transport of air cargo.

Presently cargo waits in carts on the ramp until space is available on an airplane (passengers and baggage have first priority). The rapid growth of air cargo may cause operational problems in this area so an air cargo model would be especially 99 useful at this time. It is important to notice that the above suggestions undertake to analyse one particular part or operation of the airport at a time. Models of the whole airport become too cumbersome and expensive to run, whereas smaller modular simulations can he quite inexpensive to run. 100

APPENDIX C: ADDITION OF INTERNATIONAL ftfiSIVALS MODOLE

The additions necessary in the GPSS program to accommodate International Arrivals are described below. The main problem is not with program design but with collection of

data for the model. The passengers must enter the International arrivals area, pass through -immigration if necessary and then collect •' their bags and pass through a customs inspection.

Distributions of immigration and customs processing times must

be obtained.

Although data collection may be lengthy, the motivation to add this section of the airport to the model is strong. Long gueues often exist in the customs area if two large planes arrive close together. Such a model might help coordinate airline and customs operations.

To add international arrivals to this model, much of the present logic can be used. The international passengers

walk a different route and pass through a different process.

Bags for international passengers pass through a different conveyor system. However the present carousel assignment section can be enlarged with a TEST block at line 280 to separate international and domestic bags., Assignment could be accomplished with a single SELECTMIN 5,8,9,,V. Carousels 8 and

9 would be international. Similarly, conveyor distances 8 and 9 could be included in the matrix CLGTH . The bags could then be

unloaded normally and pass through the present baggage section

to wait at the MATCH block until collected by the passenger.

The international passengers require a different 101

processing section from domestic passengers. However, some of the present logic such as leaving the plane and walking to the carousel area may still be used. At line 353 a TEST for passage through immigration can be made. If positive, then an ADVANCE fN$HAIT1 block with service time according to a certain distribution can be entered.

Returning to the present logic, at line 354 the passenger who is now through immigration can collect his bag.

Again, a TEST for international status is made at line 362. The international passengers then pass through a seguence:

QDEDE CUSTM

ADVANCE FN$WAIT2

DEPART CDSTM

ENTER PCLM2

ADVANCE FN5>BL Al

LEAVE PCLH2

A return to line 368 tabulates transit time and terminates the passenger*s path. APPENDIX D: FLOWCHART 103

Complete Flowchart

GENERATE First Plane arrives IRAN s FE 3> (FOLLO) * ,,1,350,3,12,H jA7j,.Carousel Assignment 1 .2 P1=plane type 200 (ST3T) ( ASSIGN ~J if less than 2,1 200 pass, don't P2=airline assign 2 carousels CASSIG N 3 cn 3,350 P3= i of pass, airline? f ASSIGN j in plane

4,25 choose carousel P4= gate number c ASSIGN 3 from AC side 12,2 QKSAVEVALUE^store number P12= # of f ASSIGN "3 baggage trucks 1 —1—'- '-———1 carousel assigned V11 1st pass, goes to (BEGIN % IKK JaEGIK, otners wa.it FIFO) until carousel is (K0T1) did we assigned. choose #1? SELECT -K Restart scan, other choose from pass, enter #2 & #3 user chain.

Q HSAVE VALUE + ore carousel # |ASSMT,2,?4,PT,H

1 (TYP47) Check fRAN S FE>> (AFTER) for 747? B^Q we cnoose # 2? (N0T2) (CPA) Which airline? V3 (NEG) is carousel Choose minimum ni less full loaded carousel than #3? Q KSAVEVALUE ~~3 store ?1 ASSMT,2,P4,K1,K (NEXT1)

(AFTER) >

CP gets carousel 4 ( ASSIGN } KEG^jiMSAVEVALUj E store #3 |ASSWT,2,P4,K3.H J NEXT1 C KSAVEVALUE ) t , rRANSFEJy> (AFTER) ^. ^ i ..tore carousel \ y- — Hr |ASSKT,1,P4,P5,H| number for output

V11 (FOLLO UNLINK bring forward ALL other passengers 104

N0T2 ^ SELECT choose from LAMDA 6,FKSARRCT P6= ^connecting \J5,1.2,,Y #1 & n fASSIGN 3 store # 7,FK»BAG| ( ASSIGN j P7" ' of baes

8,FNSPLEAS P8- ^business CPA C ASSIGN J CP side SPLIT (BAGS) C chooses P7 create MSAVSVALUS ) #3 & #\ xacts

AFTER B

^assign only 5,MHSASSMT(1,P4), one KH$ASSMT(2,P4),,V carousel End of Carousel Assignment (FOLLO) V11 ALL UNLINK] bring forward other pass. FOLLO »{5,MHSASSMT(1 tP4)[ ( ASSIGN 3 105

a 1

QUEUE P4) wait on plane TABULATE vVr"1 tab pass. for exit V5 1 transit time plus bag wait time. SEIZE [y^y enter plane exit QUEUE POSCI) join positive claim check DEPART leave plane queue X5> queue

ADVANCE ENTER j^cT\ enter claim FNSLVPLN time to check storage exit plane

DEPART POSCI) leave queue RELEASE vy leave plane V exit

ADVANCE Tag match FN3BLAY 2 (OUT) connecting? time

LEAVE leave claim check 0 (OUT) no bags?

tab total pass, transit ADVANCE time V$WA1K walk to carousel OUT [TERMINATE) 0| passengers leave model TABULATE V11| tab pass, transit time to claim area

( MARK "^9; P9 marks passenger 1 " bag wait time

ENTER enter carousel area

MTCHA MATCH match passenger with bag(8) X ADVANCE remove last bag FNSDLAY from carousel

LEAVE RE7 leave carousel area

TABULATE tab pass, bag wait time 106

Baggage Section of Model

All bags enter this section for 747 and L-1011 Bags random mix FIRST ADVANCE 4.75-5.75 minutes BAGS 10.RN2 315.30 to position equipment ( ASSIGN yandom^number

SPLIT plane hold queue (LATER) unload at 2 hatches

SPLIT P12 (CONTU) reserve P12 2 (FIRST) trucks is it a 747 or L-1011? P4 LINK put parent 10 xact on user chain with other bags P12 baggage trucks LATER ADVANCE 210,30 3-4 minutes to unload Join user chain each ULD pair

(SEQ2) UNLINK (NONE) ,w unhook 100| (ARCAN) _ bags from user chain if they are .there (LATER) go back CP AIR (HOLD) to unload more ULD's trigger truck pool I all ULD's ARCAN with bags dummy block does are unloaded not refuse entry -triggers truck pool leave plane hold HOLD queue wait for gate ADVANCE to be opened V12 drive to by truck conveyor

(JOIN) Join path of regular bags 10?

( SAVEVALUE 3 ONHND 13,?K^CAPCY PGATE >p4,H| ASSIGN "\ capacity J85+^!5 bags communicate c (_ SAVEVALUE J parameters PCASL,P5,H to baggage W3CPAIR 0 wait until truck pool EEST / ^ P^-8-116 section - arrives at ( SAVEVALUE ) block CPAIR PLTYP.P1,H

.,< BLOKB ENTER C SAVEVALUE ) |/ir\ busy status

LOGIC 19 open gate 1?

LOGIC ^ p|] close gate 19 again restart scan, PRIORITY bring trigger xact thru "assign" BUFFER Li,iU-II»AlSj OJ truck triggering blocks • xacts leave model

1 , XH&PLTYPI Pools of Baggage Trucks c ASSIGN 3 2.XHSAIRLN ONE / GENERATE) assigning ASSIGN relevant ,,,17,0,6 ) provide 17 AC D parameters baggage tractors communicated thru SAVEVALUES 4,XHSPGATE| truck ID # c ASSIGN 3

ACWT 5.XHSPCASL capacity 140+10 bags J (4 tubs)- ASSIGN 2 (PLN47) is it a 74V or L-1011? wait until AC plane arrives at block ARCAN GOON ADVANCE unload bag FNSOFFLD (BLOKB) SAVEVALUE ) communicate OPEN.PfrHJtruck ID to provide 10 CP bag M10,0,6, j baggage trucks P4 (SEQ1) UNLINK] (LDUP) K1 unload another \ 6.N3TWO} bag if plane not / ASSIGN-) assign truck ID # empty else go to LDUP LOOP —tzr___ Go back unload another bag if truck not fuJ,J, LDUP LOGIC, P6 open P6 gate 108

V20 wait to drive Path of Rap-ular Bags truck to conveyor F_i

SEQ1 'DEPART jQ leave plane LOGIC close gate V20 hold queue on next truck J 12.XH80PEN reassign P12 with truck ID ADVANCE . 300 delay 5 minutes to unload truck P12 wait for truck CH*4 / G (NBUSY) to be loaded if more bags are on plane return else go to NBUSY PRIORITY Bring bags thru gate P12 BUFFER (GOON)

PLN47 LOGIC ADVANCE P12| close gate P12 747's use trucks 1420,420 for 15-30 minutes ADVANCE NBUSY LEAVE leave busy status V12 drive to conveyor

LOGIC [V2151

reset PR R [—-I open truck PRIORITY release gate

JOIN * 747 bags Join here (ACWT) ENTER /^30\ seize unloading J" employee return AC and CP ADVANCE trucks to correct FNSCONLD bag unload time pools I LEAVE~ j\V30/ release employee 'RANSFER? (ONHND)

1 (OUT)

ENTER /V40\ storage

ADVANCE move on conveyor V13 to carousel

LEAVE M[4Q/ leave conveyor 109

TABULATE "v6T[ tab bag transit time Timer Segment

( MARK "TTTj start timing 1 bag wait 1— ENTER /V2*C\ enter carousel run model for storage 3 hours

gather bags of ,GATH" each passenger decrease together termination counter by 1

TABULATE tab time for all bags of each pass, to arrive

LEAVE |\V20/ leave carousel storage

ASSEMBLE combine bags of 1 pass, into 1 xact

MTCHB MATCH match bags with passenger

TABULATE tab total of bag waiting time + bag transit time

tab bag wait time only

TERMINATE) 0 bags leave model APPENDIX E: LISTING I ' ' *** GPSSV - MIS VERSION * * IBM PROGRAM PRODUCT 5734-XS2 (V1M4) *** SXA.I.EM.E.NI. NUMBER REALLOCATE BL0.175, FAC,32,FUN,51,LOG,30, FMS,1,HMS,4 1 o F AI a nr.AT F 01 IF . i inQTn.47. .FW. 1 .HSV. 7.TAR.R3? . C H A • 132 ? REALLOCATE VAR,81,BVR,1,XAC,5000,COM,282000 3 STATEMENT BLOCK NUMBER MIIMRPQ -*i nr nPFRATTON A. R. C. D. E. F, G, H. I COMMENTS _ SIMULATE 4 5 ^p VARIABLES IN MODEL b * 7 1 VARIABLE Q101+S21 BAGS IN 8 * PLANE QUEUE + BAGS IN 9 * CAROUSEL 10 2 VARIABLE Q102+S22 11 3 VARI ABLE Q103+S23 12 4 VARIABLE Q104+S24 13 14 5 VARIABLE Q 105+S25 6 VARIABLE Q106+S26 15 7 VARIABLE Q107+S27 16 17 UIA1 K FVAR I ARii F MH*ni<;TifP*>.P4J/FNAVFt. DISTANCE FROM GATE TO 18 * CAROUSEL(FT.) /' WALKING :SPEE D OF PASSENGER (FT/SEC)= TIMEiSECJ 19 =P 20 FV AR TAFW F MH£DFGTC(P5,P4)/5.0 DISTANCE FROM GATE TO 21 * CONVEYOR (FtT/AVG SPEED OF CART (3.4 MPH) = TIME (SEC) 22 13 FVARIABLE MH$£LGTH(1,P5)/1.67 CONVEYOR LENGTH (FT}/ 23 * CDCFH nc rnKiWFv.nft linn FT/MTN) = TTMF 1SFC) 24 10 VARIABLE P5+100 THESE VARIABLES ARE USED 25 20 VARIABLE P5+20 FOR INDIRECT ADDRESSING DF 26 30 VARIABLE P5+30 STORAGES AND QUEUES AND FACILITIES 2.1 40 VARIABLE P5+40 28 29 50 VARIABLE P5+50 oO 60 VARIABLE P5 + 60 70 VARIABLE P5 + 70 31 * 32 i 1 VARIABLE P4+10G THESE VARIABLES ARE USED 3.3 34 21 VARIABLE P4+200 TO SPECIFY TABLE # 31 VARIABLE P4+300 35 41 VAR I ABL E P4+400 *f> 51 VARIABLE P4+500 37 61 VARIABLE P4+6C0 38 39 81 VARIABLE P4+800 41 ft MATRIX SAVFVAilJES 42 * 43 44 DI ST •MATRIX Hi 7,32 DISTANCES FROM CAROUSELS TO GATES

INITIAL MHK ltl J ,395/MHH2, 1) ,403/MHK 3 ,1 ) ,440 46 47 INITIAL M H11 4 ,1) ,485/MHU 5, 1),650/MHH6,1) ,605 48 INITIAL MHK 7 ,1) ,575/MHKl,2if525/MH1(2,2).533 INITIAL MH1<3,2) ,570/MHK4,2) , 615/MH1 ( 5 »2 ) ,780 49 INITIAL MHK 6, 2.) , 735/MH 1(7,2), 705/MHK 1,3} ,580 50 INITIAL MH1(2,3) 51 INITIAL MHK 5, 3.) ,835/MHK6,3).790/MH1I7,3) ,760 52 INITIAL MH1 { 1 ,4!) ,715/MHK2,4) ,723/MHU 3,4} , 760 5 3 INITIAL MH1(4,4) ,805/MHK 5.43 .970/MHK6 ,4) ,Q?5 54 I NIT IAL MHI(7,43,895/MHK1,5),760/MHl(2,5 3,768 55 INITIAL MHI13,53,805/MHI(4,5),850/MHl{5,5),1015 56 INITIAL J!tLUJ5uL5JL^ 57 INITIAL MHH 2, 6 3,898/MHK 3, 6 ) , 935/MH K 4, 63 , 980 58 INITIAL MH1<5,6),1145/MH1{6,6),1100/MHI(7,63,1070 59 INITIAL MHlt1,7 3,940/MHU?. 73.948/MH1(3.73,985 60 INITIAL MHI (4,7), 103 0/MHH5,7) , 1195/MH1 (6 , 73 ,1150 61 INITIAL MHlt7,7 3,112 0/MHK 1 ,83 ,940/MHK 2 ,8),948 62 INITIAL 63 INITIAL MHI16 , 8),1150/MH1 ( 7,8 3 ,1120/MHI(1,9),940 64 INITIAL MH112,9),948/MHI(3,93,985/MHK4,9),1030 65 INITIAL MHI15,93,1195/MHI(6,93, 11507MH1(7,93, 1 120 66 INITIAL MHK 1, 103 ,360/MHK 2, 103 ,368/MHI (3,10) ,405 67 INITIAL MHU 4, 10),450/MHK5, 10) , 615/MHK6, 103 ,570 6 8 INITIAL MHI<7, 10),540/MHl(1, 123 ,560/MH1 (2, 12 3 , 568 69 IN ITIAL MHU 3 , 123 , 605/MHI (4, 123 ,650/MHl j 5 ,12 3 , 815 70 INITIAL MHI(6,12),?70/MHK 7,12),740/MHK1,133,610 71 INITIAL MHI(2,133,618/MHl(3, 13 3 , 655/MH1<4,133 .705 72 INITIAL MHU 5, 13),865/MHU6, 13),820/MH1(7,133 , 790 73 INITIAL MHK1 ,20 3,738/MH1(2,203,768/MHl(3,203 , 738 74 INITIAL MjHJLL4jt20i,723/MHl<5f 20) ,498/MHl 16,203 ,543 75 INITIAL MHK 7,203,588/MHK 1, 213 , 750/MH1(2,213,780 76 INITIAL MH1<3,21),750/MHl<4,21),735/MH1 (5,21),510 77 INITIAL MHK6,21) ,555/MHK 7,21) »540/MH1 11 ,22 3 ,930 78 INITIAL MHK 2 , 22 3 , 960/MHK3 , 223 ,930/MH1(4,223 ,915 79 INITIAL MHI(5,22),690/MHU6,223,735/MHli7,223,780 80 INITIAL MMJLLLL2JLJJL23J0/MHI(2,233, 960/MH 1(3,231 ,930 8 1 INITIAL MHI(4,23),915/MHI(5,23 3,690/MH1<6,23), 735 82 INITIAL MHI(7,233,780/MHK1,24),1100/MH1(2,24),1130 83 IN ITT Al MHU 3, 24), 1100/MH.lf 4.24J . 1085/MHK 5,743 .860 84 INITIAL MHI (6,24) ,90 5/MHH7,24 3,950/MHUl,25 3 , 1110 85 INITIAL MHU 2, 25) , 1140/MHH 3,253 ,1110/MHi(4,25),I 095 86 INITIAL MHl.15.,251, 87 0/MHK 6,253,915/MH K 7,25),960 87 INITIAL MHK 1,263 » 1190/MHI {2 ,26 3 , 1220/MHI (3,263 , 1196 88 INITIAL MHU 4,26), 1175/MHK 5,263 ,95 0/MH 1(6,26) ,995 89 INITIAL MH1(7,26),1040/MHK1.271.1150/MH1l?*77 1.13 80 90 INITIAL MHK3,27),1150/MH1(4,27),1135/MH1(5,27),910 91 INITIAL MHU 6, 27) ,955/MHK 7, 27) , 1000/MH 1(1, 303 ,63 5 92 INITIAL MH1.I.2 ,3.0)..J.665/MHl.i3.TJ0.1..,635/MHi (4, 30), 620 93 INITIAL MHK5,30),39 5/MHl(6,30),440/MH1(7,30),485 94 INITIAL MHK.1, 32 3, 890/MHI ( 2, 32 3 , 920/MH1 < 3 ,32 3 , 890 95 INITIAL MHK4.323.875/MH115.32),650/MHl(6,32).695 96 INITIAL MHK 7,323 ,740 97 98 OFGTC MATRIX H».7,32 DISTANCE FROM GATES TO CONVEYOR AREA 99 BY CART * 100 INITIAL MH2(1,13 ,25 0/MH2(2—4,13,2007MH2(5-7, 13,300 101 INITIAL MH2( 1,2),300/MH2(2-4,23 .300/MH2I 5-7,2),46 0 102 INITIAL MH2U,3) ,4O0/MH2( 2-4, 3 3 ,400/MH2 <.5-7, 33 , 500 103 INITIAL MH2<1,43,400/MH2(2-4,43,400/MH2(5-7,4),500 104 IN I TIAL MJH21JL.t5 ),500/MH2( 2-4,5 3 , 500/MH2 ( 5- 7, 5 3 ,500 105 INITIAL MH2{1,6) ,540/MH2{2-4,6),54O7MH2(5-7,6),640 106 INITIAL MH21l,7 3,640/MH2(2-4,73,640/MH2(5-7,73,640 107 INITIAL MH21l,8i),700/MH2(2-4,8),700/MH?{5-7,83,700 108 INITIAL MH2(1,9),7O0/MH2(2-4,9),700/MH2(5-7,9),750 109 INITIAL MH2(1,10) ,500/MH2(2-4,10),600/MH2(5-7,10) ,840 110 INITIAL M.H.2^ ( 2-4, 12 3 ,9 50/MH2( 5- 7, 12 3 , 1140 111 INITIAL MH2 ( 1, 133 ,90 07MH2 ( 2 -4'•, 13 ji , 1166 / M H2 (5-7,13.) , 1306 112 INITIAL MH2( 1,2 0) ,300/MH2{ 2-4,203 .250/MH2 I 5-7,20) ,2 00 113 INITIAL MH2 ( 1 ,213 ,400/MH2 (2-4,21 ) , 3 50/M H2 ( 5-7. 2 1 ) ,300 114 4 113 INITIAL MH2< 1 ,22) ,40C/MH2l 2~4,22) ,350/MH2 ( 5-7,22) ,300 115 INITIAL MH2U,23),500/MH2(2-4,23) ,500/MH2{ 5-7, 23) ,450 116 INITIAL MH2(.1, 24 ).,.5Q0/.MH ill INITIAL MH2(1,25),600/MH2(2-4,25),600/MH2(5-7,25),600 118 INITIAL MH2U,263,600/MH2i2-4,26),600/MH2(5-7,26),600 119 INITIAL MH2I 1.27) .650/MH2 ( 2-4. 27 ) .650/MH2 { 5-7 .27 ) .650 120 INITIAL MH2I 1, 30),600/MH2(2-4,30) ,600/MH2( 5-7,30) ,500 121 INITIAL MH2(1,32) ,650/MH2i2-4,32),650/MH2I 5-7,32),550 122 CLGTH MATRIX H, 1, 7 CONVE..YQ.R LENGTHS TO LAR.QUS.E.LS. - .123.... INIT IAL MH3(1,1),35/MH3(1, 2 j ,18 0/MH3(1,3), 165 124 INITIAL MH3(1, 4 ) , 180/MH.3 (1,5),210/MH3J1,6),210 125 126 INITIAL MH3<1.71.180 127 ASSMT MATRIX H,2,32 SAVEVALUES OF CAROUSELS ASSIGNED 128 TO EACH FLIGHT ACCORDING TO GATE 129. 130 131 STORAGES IN MODEL 132

STORAGE Sl-S7,500 P5 STORAGE: SHOWS # OF PEOPLE 133 AT CAROUSEL 134 13 5... STORAGE S21-S27,300 136 CAPACITY OF BAGGAGE CAROUSEL (CONTENTS NOT TRUE 137 •* REFLECTION DUE TO 'ASSEMBLE* BLOCK) 138 STORAGE S31-S37,2 UNLOADING EMPLOYEE(S) 139 140 SJGRAGE S41-S4J..,25 CONVEYOR C APAC.I T I.ES... .1.4.1... 142 143 STORAGE EMPLOYEE!S) AT +VE CLAIM CHKPT 144 145 STORAGE 27 146 ...1.4.7.. PARAMETERSINmOEL 148 149 * PI = PLANE TYPE 1=747t 2=L-1011 3=DC-10 4=0THER 150 * P2 = AIRLINE 1=AIR CANADA 2=CP AIR 151 * P3 = # CF PASSENGERS IN PLANE GROUP 152 * P4 = GATE NUMBER .1.5.3 * P5 = CAROUSEL ASSIGNED 154 * P6 = 1=ARRIVING, 2=C0NNECTING 155 * P7 = # OF BAGS / PASS. 156 * P8 = EBUSINESS, 2=VACATION 157 * P9 = BAG WAITING TIME PER PASSENGER 158 * P10 = RANDOM NUMBER ASSIGNED TO BAGGAGE ...159... * Pll = BAG WAITING TIME AT CAROUSEL (=MAXIMUM TIME UNTIL 160 * ALL BAGS OF 1 PASSENGER ARRIVE AND ARE MATCHED 161 * WITH PASSENGER) 162 * P12 = # GF BAGGAGE TRUCKS ASSIGNED TO PLANE 163 * (MINIMUM 2 FOR REGULAR FLIGHT, 3 FOR A 747) 164 165. TABLES IN MODEL 166 167 112 TABLE Ml,30,30,120 PASSENGER TRANSIT TIME 168 * TO CLAIM AREA 169 212 TABLE MP9,30,30,120 PASS. BAG WAITING TIME 170' .312 TABLE Ml , 3 0 30, 120 P AS S ENGER TR.ANS.I.I TIME .+ PASS. ...17.1... "*BAG WAITING TIME 172 412 TABLE Ml,30,30,120 TOTAL PASSENGER TRANSIT 173 * TIME (INCLUDING POSITIVE CLAIM f.K 174 JiiL 512 TABLE M 1,30,30,120 TIME FOR ALL BAGS OF A PASS. 175 TO ARRIVE AT CAROUSEL 176 .612 TABLE M 1.^.30 ,30 ,12 Q. BAG I RAN.S.II TIME ID CARQUS EL ill... 712 TABLE MPH,30,30, 120 BAG WAITING TIME AT CAROUSEL 178 812 TABLE Ml ,30 ,30,120 TOTAL OF BAG WAITING TIME + 179 BAG TRANSIT TIMF 180 181 113 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 182 213 TABLE MP9,3C ,30 ,120 BAG WAITING TIME AT CAROUSEL 183 313 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 184 413 TABLE Ml ,30, 3 0, 12 0 TIME FOR ALL BAGS OF A PASS. 185 513 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 186 613 TABLE Ml,30,30,120 TIME FOR ALL BAGS OF A PASS. 187 713 TABLE MP11,30,30,120 BAG WAITING TIME AT CAROUSEL 188 813 TABLE ...M 1,30,30, 120 TIME FOR ALL BAGS OF A PASS. 189 104 TABLE Ml,.30, 30, 120 TIME FOR ALL BAGS OF A PASS. 190 204 TABLE MP9,30 ,30 ,120 BAG WAITING TIME AT CAROUSEL 191 304 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 192 404 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 193 504 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 194 604 TABLE Ml,30, 30, 120 TIME FOR ALL BAGS OF A PASS. 195 704 TABLE MP11,30,30,120 BAG WAITING TIME AT CAROUSEL 196 804 TABLE Ml,30,30,120 TIME FOR ALL BAGS OF A PASS. 197 * FUNCTIONS IN MODEL 198 199 ARRCT FUNCTION P4, E3 ARRIVING OR CONNECTION {DEPENDS ON 200 4,FN20/12,FN20/13 ,FN21 PLANE AT PARTICULAR GATE} 201 •* 202 RN2,D2 1= ARR I V ING 2=C0NNECTI0N 203 20 FUNCTION 204 .90,1/1.0,21 FUNCTIO2 N RN2,D2 DITTO 205 .900,1/1.0,2 206 207 * BAG FUNCTION P8, E2 # OF BAGS DEPENDS ON 208 1,FN30/2,FN31 BUSINESS OR PLEASURE 209 30 FUNCTION RN2.D.5 BAGS / BUSINESS PASS. 210 .089,0/.6U,1/.935,27.984,3/1.0,4 211 31 FUNCTION RN2,D5 BAGS / VACATIONER 212 .Q.43,0/,6 24,1/.952,2/.996,3/1.0,4 213 214 * P4,E3 PLEASURE, VACATION RATIO DEPENDS 215 PLEAS FUNCTION ,FN41 CN PARTICULAR FLIGHT 216 40 FUNCTION 4,FN40/12,FN40/13 RN2,D2 1 = BUSINESS 2 = PLEASURE 217 . 5, 1/1.0, 2 218 41 FUNCTION RN2,D2 DITTO 219 .5,1/1.0,2 220 * 221 P2,E2 TIME TO EXIT PLANE DEPENDS ON PLANE TYPF LVPLN FUNCTION 222 3,FN50/4,FN51 223 50 FUNCTION RN2,D2 FASTER EXIT TIME ON 747,L-1011 L 224 .965,1/1.0,2 DC-10. 2 DOORS AVAILABLE, EACH AT 29 PASS/MIN OR 1 PASS/2.069 SEC 226 51 FUNCTION RN3,D3 REGULAR PLANE EXIT TIME 227 .05,1/.831,2/1.0, 3 AVG. 1 PASS. EVERY 2.069 SEC 22 8 * 229 PASS. WALKING SPEED DIST'N 230 VEL FUNCTION RN2.0.5 r..5 .01,1/.25,2/.60,3/.98,4/1.0, RN2tC2 TIME TO REMOVE BAGS FROM CAROUSEL 232 0,3/1,50 VARIES UNIFORMLY 8ETWEEN 1-50 SEC 233 ( PERIOD OF 1 REV. OF CAROUSEL ) ?34 BLAY FUNCTION RN2,D7 TIME TO MATCH BAGGAGE TAGS AT CHECKPT 23 5

.125 tl/. 25,2/. 5 8,3/.705,4/. 83, 5/. 955, 7./1.0, 18 236 * 237 OFFLD FUNCTION RN2,02 CDF FOR UNLOADING BAGS FROM PLANE 23 8 .5,7/1.0,8 AVG. 7.5 SEC/BAG 239 * 240 J CONLD FUNCTION RN5,D3 UNLOADING BAGS FROM 241 < .33,5/.67,6/1.0,7 CART TO CONVEYOR AVG. 6 SEC/BAG 242 243 CAPCY FUNCTION RN4,C2 RANDOM ASSIGNMENT OF 244 0,80/1.0,91 TRUCK CAPACITY 80-90 BAGS 245 246 ACCAP FUNCTION RN5,C2 CAPACITY OF AC TRUCKS 247 0,130/1.0,151 TRUCK CAPACITY 130-150 BAGS C4 TUBS) 248 249 * MODEL LOGIC BEGINS 250 251 252 * PLANE TRANSACTIONS ARE ENTERED IN THE MODEL USING 253 * "GENERATE" BLOCKS IN THE FORM BELOW: 254 * GENERATE A,B,C,D,E,F 255 * A = 0 256 * B = 0 257 * C = TIME 1ST PLANE OF THAT TYPE IS TO ARRIVE 258 * AND NO PLANES CAN ARRIVE CLOSER THAN 1 SEC TO 259 * EACH OTHER. 260 * D = # OF PASSENGERS TO ARRIVE ON PLANE 261 * E = 3 START WITH PRIORITY 3 262 * F = 12 PARAMETERS 263 * G = H , HALFWORD PARAMETERS 264 * FOLLOWED BY 3 "ASSIGN" BLOCKS TO DEFINE PARAMETERS 265 * 1,3 AND 4J THEN A "TRANSFER ,AAA" IS NEEDED TO 266 * ENTER THE MODEL PROPER. 267 * 268 1 GENERATE ,,1,300,3,12,H PLANE AT T=l 269 2 ASSIGN 1,K1 Pl=PLANE TYPE 270 .3 ASSIGN 2,K2 P2=AIRLINE 1=AIR CANADA 2=CP AIR 2 7.1 4 ASSIGN 3,K30C P3=# OF PASS. IN PLANE GROUP 272 5 ASSIGN 4,K12 P4=GATE NUMBER (1

19 SELECTMIN 5,1,3,tV AIR CANADA SIDE 297 20 MSAVE VALUE ASSMT,1, P4»P5,H ASSIGN 2 CAROUSELS FROM 298 21 TEST E P5,K1,N0T1 #1, #2, AND #3 299 22 SELECTMIN 5,2.3, ,V 3 00 j 23 M SAVE VALUE ASSMT,2,P4,P5,H 301 < 24 TRANSFER , AFTER 302 * 303 25 NOT1 TEST E P5,K2,NCT2 304 26 TEST L V1,V3,NEG 3 05 27 MSAVEVALUE ASSMT,2 ,P4,K1.H 3 06 28 TRANSFER ,AFTER 307 29 NEG MS AV EVALUE ASSMT,2,P4,K3,H 308 30 TRANSFER ,AFTER 309 310 31 NGT2 SELECT MIN 5,1,2,,V 311 32 MSAVEVALUE ASSMT,2,P4,P5,H 312 33 TRANSFER , AFTER 313 * 314 34 CPA2 MSAVE VALUE ASSMT,1,P4,K4,H 315 35 MSAVEVALUE ASSMT,2,P4,K3,H CP SIDE, ASSIGN #3 £ #4 316 * 317 36 AFTER ASSIGN 5,MH$ASSMT(2,P43 318 37 TEST L V*5,280,USLSS CHECK THAT 2ND CAROUSEL 319 * ASSIGNED HAS < 280 BAGS IN 320 * ITS SYSTEMj IF NOT, NO SENSE i * DIVIDING PASSENGERS AMONG 2 CAROUSELS 322 38 UNLINK Vll,BETA,ALL BRING FORWARD OTHER PASS. ' 323 39 BETA TRANSFER .5,GAMMA,DELTA DIVIDE PASS. AMONG 2 CAROUSE! S 374 40 GAMMA ASSIGN 5,MH$ASSMTU,P4) 32 5 41 TRANSFER , LAMDA 326 42 DELTA ASSIGN 5,MH$ASSMT{2aP4) 327 43 TRANSFER ,LAMDA 32 8 * 329 44 USLSS SELECT MIN 5,MH$ASSMT(1,P43.MH$ASSMT{7.P4)..V MAKE SURE YOU 330 i * HAVE THE SHORTEST 331 1 * CAROUSEL LINEUP. 332 ; 45 UNLINK Vll,FOLLO,ALL BRING FORWARD OTHER PASS. 333 46 FOLLO ASSIGN 5,MH$ASSMTU,P4 3 334 47 LAMDA ASSIGN 6,FN $ A RRC T P6=l ARRIVING, P6=2 CONNECTING 33 5 48 ASSIGN 7,FN$8AG P7=# OF BAGS OF PASS. 33 6 49 ASSIGN 8,FN$PLEAS P8=l BUSINESS, P8=2 VACATION 337 338 50 SPLIT P7,BAGS CREATE BAG XACTS; SEND TO "BAGS" i 340 * PASSENGER SECTION OF MODEL 341 34? 51 PAXR QUEUE P4 QUEUE OF PEOPLE ON PLANE AT GATE P4 343 52 SEI ZE P4 ENTER EXIT AT GATE P4 344 53 DEPART P4 345 54 ADVANCE FNSLVPLN LEAVE PLANE IFUNCT. DEPENDS ON PLN TYPE 3 346 5:5 RELEASE P4 LEAVE EXIT 347 56 TEST NE P6,2,0UT CONNECTING PASS. LEAVE MODEL HERE 348 57 TEST NE P7,0,0UT PASS. W/0 BAGS « " " 349 58 ADVANCE V$WALK WALK TIME TO CAROUSEL DEPENDS ON 350 GATE #, CAROUSEL #, AND VELOCITY DIST'N. T=D/V 351 59 TABULATE Vll TABULATE PASS. TRANSIT TIME TO 352 CLAIM AREA 353 60 MARK 9 START MEASURE OF WAIT TIME FOR BAGS 354 > j i in 61 ENTER P5 ENTER CAROUSEL STORAGE (CAP = 500) 3 55 62 MTC HA MATCH MTCHB MATCH PASS. WITH ALL HIS/HER BAGS 356 63 ADVANCE . FNSDLAY TIME TQ GET BAGS OFF CAROUSEL IS 357 * RANDOMLY SELECTED FROM TIME INTERVAL 358 1 - 50 SEC I PERIOD OF REV. OF CAROUSEL 359 64 LEAVE P5 LEAVE CAROUSEL AREA 36 0 J 65 TABULATE V21 TAB BAG WAITING TIME PER PASS. 361 < 66 TABULATE V31 TAB PASS. BAG WAIT TIME PLUS 362 TRANSIT TIME 363 67 QUEUE POSCL WAIT AT POSITIVE CLAIM CHECKPT 364 68 ENTER 8 CLAIM CHK STORAGE (1 OR MORE STAFF.) 36 5 69 DEPART POSCL 366 70 ADVANCE FN$BLAY TAG MATCH TIME DEPENDS ON # OF BAGS 367 71 LEAVE 8 368 72 TABULATE V41 TAB TOTAL TRANSIT TIME OF PASS. 369 73 OUT TERMINATE PASS. LEAVES MODEL 370 * 371 * BAGGAGE SECTION OF MODEL 372 -•V 3 73 * 74 7 AND L-1011 BAGS 3 74 * 375 74 FIRST ADVANCE 315,30 4.75- 5.75 MINUTES TO POSITION 376 If* UNLOADING EQUIPMENT 37 7 75 SPLIT 2,LATER UNLOAD AT 2 HATCHES 378 76 SPLIT 2,C0NTU RESERVE 2 TRUCKS 379 77 LINK P4,10 PUT PARENT TRANSACTION ON USER CHAIN 380 J, T WITH OTHER BAGS 381 78 LATER ADVANCE 210,30 3-4 MINUTES UNLOADING EACH 382 * PAIR OF ULD*S 383 79 UNLINK P4,SEQ2»X10Q,,« NONE UNHOOK 100 BAGS FROM 3 84 THE USER CHAIN IF THEY ARE THERE 385 80 TRANSFER ,LATER GO BACK TQ UNLOAD MORE ULD'S 3 86 81 NONE TERMINATE ALL BAGS HAVE BEEN UNLOADED 82 SEQ2 DEPART VI0 LEAVE PLANE HOLD 38 8 83 ADVANCE V12 DRIVE TO CONVEYOR 389 84 TRANSFER , JOIN JOIN PATH OF REGULAR BAGS NOW 390 391 * THIS SECTION FOR RANDOM MIX 392 * ALL BAGS ENTER 393 85 BAGS ASSIGN 10,RN2 RANDOM NUMBER IN PI 0 3 94 86 QUEUE V10 PLANE QUEUE 395 87 LINK P4,10,LINE 1ST XACT GOES TO BUFFER; NEXT XACTS 396 * ARE PUT ON CHAIN P4 IN RANDOM ORDER 397 88 LINE TEST G PI,2,FIRST IS IT A 747? 398 89 SPLIT P12,CCNTU 399 90 LINK P4,10 400 91 CCNTU TEST E P2,2,ARCAN 401 92 CPAIR TRANSFER , HOLD 402 93 ARCAN ADVANCE 0 403 94 HOLD GATE LS 19 404 95 SAVEVALUE PGATE,P4,H COMMUNICATE PARAMETERS 405 96 SAVEVALUE PCASL,P5,H TO BAGGAGE POOL SECTION 406 ... —„• 97 SAVEVALUE PLTYP,P1,H 407 98 SAVEVALUE AIRLN,P2,H 408 *'"' 99 LOGICR 19 409 100 TERMINATE 410 A- -a* * POOLS OF BAGGAGE UNLOADING TRUCKS 412 '* 413 1PJ ONE GENERATE ,.,17,0.6 PROVIDE 17 AC TRUCKS 414 119 102 ASSIGN 6,N$GNE TRUCK ID # 415 103 ACWT ASSIGN 3,FN$ACCAP CAPACITY 140+-10 BAGS 416 104 TEST G W$ARCAN,0 WAIT FOR AC PLANE TO ARRIVE 417 '105 TRANSFER ,BLOKB 418 419 106 TWO GENERATE ,,,10,0,6 PROVIDE 10 CP TRUCKS 420 107 ASSIGN 6,N$TW0 TRUCK ID # 421 1 108 ONHND ASSIGN 3,FN$CAPCY CAPACITY 85+-5 BAGS 422 109 TEST G W$CPAIRfO WAIT FOR CP PLANE TO ARRIVE 423 * 424 110 BLOKB ENTER 9 BUSY STATUS 425 111 LOGICS 19 OPEN STOP GATE 426 112 PRIORITY 1, BUFFER RESTART SCAN 427 113 - ASSIGN 1 ,XH$PLTYP 42 8 114 ASSIGN 2, XH$AIRLN 429 115 ASS IGN 4,XH$PGATE 430 116 A S SIGN 5 ,XH$PCASL 431 117 TEST G P1,2,PLN47 IS IT A 747? 432 118 GOON ADVANCE FN$OFFLD UNLOAD BAG 433 119 SAVEVALUE 0PEN,P6,H COMMUNICATE TRUCK ID # 434 120 UNLINK P4,SEQ1,K1,*,LDUP 435 121 LOOP 3,GOON UNLOAD ANOTHER BAG IF NOT FULL 436 122 LDUP LOGIC S P6 OPEN P6 GATE 437 123 GATE LS V20 438 124 LOGICR V20 439 ! 125 ADVANCE 300 DELAY CART FOR 5 MINUTES TO UNLOAD 440 126 TEST G CH*4,0,NBUSY 441 127 ADVANCE V12 GO BACK TO PLANE 442 128 TRANSFER , GOON 443 129 PLN47 ADVANCE 1380,420 USE TRUCKS FOR 15-30 MINUTFS WITH A 747 444 130 N8USY LEAVE 9 LEAVE BUSY STATUS 445 131 PRIORITY 0 RESET PRIORITY OF TRUCK 446 132 TEST E P2,2,ACWT RETURN AC AND CP TRUCKS SEPARATELY 44 7 133 TRANSFER ,ONHND 44 0 * 449 * PATH OF BAGS 450 451 134 SEQ1 DEPART ViO LEAVE PLANE HOLD 452 135 ASSIGN 12,XH$CPEN TRUCK ID # 136 GATE LS P12 WAIT FOR TRUCK TO BE LOADED 454 137 PRIORITY 1,BUFFER RESTART SCAN, BRING BAGS THRU GATE P12 455 138 LOGICR P12 CLOSE P12 GATE 456 139 ADVANCE V12 DRIVE TO CONVEYOR 45 7 140 LOGICS V20 OPEN TRUCK RELEASE GATE 458 141 JOIN ENTER V30 SEIZE UNLOADING EMPLOYEE 459 142 ADVANCE FN$CCNLD 460 143 LEAVE V30 RELEASE EMPLOYEE 461 144 TEST £ . P6,1,0UT CONNECTING BAGS LEAVE MODEL HERE 462 145 ENTER V40 CONVEYOR STORAGE 463 146 ADVANCE V13 MOVE ON CONVEYOR TO CAROUSEL 464 147 LEAVE V40 465 148 TABULATE V61 TAB BAGGAGE TRANSIT TIME 466 149 MARK il START TIMING BAG WAITING TIME 467 150 ENTER V20 ENTER STORAGE CAROUSEL 468 151 GATHER P7 GATHER BAGS OF EACH PASS. TOGETHER 46 9 152 TABULATE V51 TAB TIME FOR ALL BAGS OF EACH PASS. 470 * TO ARRIVE 153 LEAVE V20 LEAVE CAROUSEL 472 154 ASSEMBLE PI COMBINE BAGS OF 1 PASS. INTO 1 XACT 473 155 MTCHB MATCH MTCHA NATCH BAGS WITH PASSENGER 474 j 156 TABULATE V81 TAB TOTAL OF BAG WAITING TIME + 475 BAG TRANSIT TIME 476 ...1,5.7 TABULATE V71 TAB BAG WAITING TIME AT CAROUS EL 477 158 TERMINATE 4 78 479 159 GENERATE 3600 480 160 TERMINATE 1 481 RMULT 245,245,245,245,245,245,245,245 SET RANDOM # SEEDS 482 START 1 ...481.. 484

J 120

APPENDIX F: OUTPUT

The typical output from a run of this simulation model is enclosed in the following pages. Statistics are presented on many parts of the system. For example the QUEUE POSCL shows average waiting time and maximum number of entries in the gueue at the positive checkpoint.

The key for facility, storage and table numbers is obtained by examining the listing. Storages are on lines 131 to

146, tables are on 168 to 190.

Graphic statistics such as the example on the last page of this section may be obtained by including the seguence:

REPORT

OUTPUT

GRAPH TR,510

ORIGIN 50,10

X ,, 1,30,,54,NO

Y 0,5,20,2

50 STATEMENT 1,7,TR: 510

ENDGBAPH after the START statement for each graph., The graphs are of tables 1 to 8. RELATIVE CLOCK 3600 ABSOLUTE CLOCK 3600 BLOCK COUNTS BLOCK CURRENT TOTAL BLQCK TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL 1 0 300 11 0 0 21 0 0 31 0 0 41 0 179 2 0 300 12 0 0 22 0 0 32 0 0 42 0 121 3 0 300 13 0 0 23 0 0 33 0 0 43 0 121 J 4 0 300 14 0 0 2 4 0 0 34 0 1 44 0 0 5 0 3 00 15 0 0 25 0 0 35 0 1 45 0 0 6 0 300 16 0 0 26 Q ...... Q 3.6.... ; 0 . .. 1 46 0 0 7 0 300 17 0 1 27 0 0 37 0 1 47 0 3 00 8 0 1 18 c 1 2 8 0 0 38 0 1 48 0 300 9 0 1 19 0 0 29 o 0 39 0 300 49 0 300 10 0 0 20 0 0 30 0 0 40 0 179 50 0 704

BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL 51 0 300 61 0 250 71 0 250 81 0 2 91 0 2 52 0 300 62 0 250 72 0 250 82 0 404 92 0 2 53 0 300 6.3 0 250 73 0 330 83 0 404 93 0 0 54 0 300 64 0 2 50 74 0 1 84 0 404 94 0 2 55 0 3 00 6 5 0 250 75 0 3 85 0 404 95 0 2 5 6 0 300 66 0 250 76 0 3 86 0 404 96 0 2 57 0 274 67 0 250 77 0 1 87 0 404 97 0 2 5 8 0 250 68 0 250 78 0 7 88 0 1 98 0 2 59 0 250 69 0 250 79 0 7 89 0 0 99 0 2 60 0 250 70 0 250 80 0 5 90 0 0 100 0 2

BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT ...... I.OI.AL 101 0 17 111 0 2 121 0 0 131 0 2 141 0 404 102 0 17 112 0 2 122 0 0 132 0 2 142 0 404 103 17 17 113 0 2 123 0 0 133 0 2 143 0 404 104 0 G 114 0 2 124 0 . 0 134 0 0 144 0 404 105 0 0 115 0 2 12 5 0 0 135 0 0 145 0 374 106 0 10 116 0 2 126 0 0 136 0 0 146 0 374 107 0 10 117 0 2 127 0 0 137 0 0 147 0 3 74 108 10 12 118 0 0 128 0 0 138 0 0 148 0 374 109 0 2 119 0 0 129 0 2 139 0 0 149 0 374 1.10 0 2 120 0 0 130 0 2 140 0 0 150 0 374

BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TQTAL BLOCK CURRENT TOTAL 15 1 6 374 152 0 374 15.3 0 374 154 0 250 155 0 250 156 0 250 15 7 0 250 158 0 250 • 159 0 1 16 0 0 .1

•J*- Vbst si, >1- %l/ A X J, X s3r sJU A- ss- V> J> s*» vA, J, st. sJs> sj, >J« s4~ -st, -A- «U «JL> -sJ'- st^ «V>W« A „!, JL- JL. si,

S.V

* USER CHAINS *

USER CHAIN TOTAL AVERAGE CURRENT AVERAGE MAX IMUM ENTRIES TIME/TRANS CONTENTS CONTENTS CONTENTS 12 404 657.883 73.829 404 12 ?

****************************************

* FACILITIES ' *

****************************************

-AVERAGE UTILIZATION DURING- ••••••••• TOTAL AVAIL. UNAVAIL. CURRENT PERCENT TRANSACTION NUMBER TY NUMBER AVFRAGE ENTRIES TIME/TRAN TIME TIME TIME STATUS AVAILABILITY SEIZING PREEMPTING •300 i .n?^ .rift's 100.0

**************************************** * * * STORAGES * * * ****************************************

-AVFRAGF UTILIZATION DURING- AVERAGE TOTAL AVAIL. UNAVAIL. CURRENT PERCENT CURRENT MAXIMUM E CAPAC ITY AVERAGE ENTRIES CONTENTS TIME/UNIT TIME TIME TIME STATUS AVAILABI LITY CONTENTS CONTENTS 500 24.9 92 105 856.866 .049 1Q 0... 0 JLM... 500 39.419 145 978.676 .078 100.0 145 .3 .306 250 4.400 .101 100.0 3 ) 27 .886 2 1595.500 .032 100.0 2 38 \ 300 4.227 163 93.350 .014 100.0 43 e 300 6.085 211 103.825 .020 100.0 2... 2 Y 2 .3 81 234 5.863 .190 100.0 25 4.4 37 163 98.000 .177 100.0 25 21 1 107.000 .250 100.0 ... ?5 «— " — • — — • • II —II — A .711 ****************************************

* QUEUES & * * ****************************************

ZERO PERCENT AVERAGE ^AVERAGE TABLE CURRENT MAXIMUM AVERAGE TOTAL ENTRIES ZEROS TIME/TRANS TIME/TRANS NUMBER CONTENTS CONTENTS CONT ENTS ENTRIES 3 .018 250 224 89.5 .263 2.538 299 12.85 8 300 1 .3 154.306 154.822 170 234 42.943 234 .0 660.666 660.666 T IME/TRANS = AVERAGE T IME/TRANS EXCLUDING ZERO ENTRIES

<

* FACILITIES ' * * _ * **** ** * ** * *** ********* ** **:*** *** * * * **** *

-AVERAGE UTILIZATION DURING- FAC111 ^YNUMBER'AVERAGE TOTAL AVAIL. UNAVAIL. 'CURRENT PERCENT TRANSACTION NUMBER ENTRIES TIM E/TRAN TIME TIME TIME STATUS AVAILABILITY SEIZING PREEMPTING 12 300 1.023 .085 100.0

:***:* **************************** ******** * * * STORAGES *

* ; * !jC -tjt sjc J(S $f. v v v r -v ^ o* v >{£ ^£ ?Jc j*Jt5jc^C3J£i^t^i;^;$;^;^c^.^c sjc sf: z£. s\z $z i[s

-AVERAGE UTILIZATION DURING- STORAGE CAPAC ITY AVERAGE ENTRIES AVERAGE TOTAL AVAIL. UNAVAIL. CURRENT PERCENT CURRENT MAXIMUM CONTENTS TIME/UNIT TIME TIME TIME STATUS AVAILABILITY CONTENTS CONTENTS 3 500 24.9 92 105 856.866 .049 100.0 104 4 500 39.419 145 978.676 .078 100.0 145 8 3 .306 250 4.400 .101 100.0 3 9 27 .886 2 1595.500 . 032 100.0 2 23 300 4.227 16 3 93.3 50 .014 100. 0 38 24 300 6.085 211 103.825 .02 0 100.0 43 33 2 .284 170 6. 012 .14.1 100.0 .2 34 2 .381 234 5 .863 .190 100.0 2 43 25 4.4 37 163 98.000 . 177 100.0 25 44 25 6.2 71 211 107.000 .250 1 00.0 ?S

»Jl* «JV «JU- mjf *Jm> ^,1*

QUEUE MAXIMUM AVERAGE TOTAL ZERO PERCENT AVERAGE $AV ERAGE TABLE CURRENT CONTENTS CONT ENTS ENTRIES ENTRIES ZEROS TIME/TRANS TI ME/TRANS NUMBER CONTENTS POSCL 3 .018 250 224 89.5 .26 3 2.5.3 8 12 299 12.858 300 1 .3 154.306 154.822 103 170 3 0.979 170 .0 656.046 656.046 104 234 42.943 234 .0 660.666 660.666 $AV ERAGE T IME/TRANS = AVERAGE T IME/TRANS EXCLUDING ZERO ENTRIES > 123 \ "V 4* A* ^ ^-fc & ^*

* TAB! FS * * j. ft************** ^: ^: ^: $ $

/ \

TABLE 112 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 25 0 369.315 119.562 92329.000 NON-WEIGHTED

UPPER OBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVI AT ION LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 .00 .0 100.0 .081 -2.837 60 0 .00 . 0 100.0 .162 -2.587 90 0 .00 .0 100.0 .243 -2.336 120 0 .00 .0 100.0 .324 -2.085 150 0 .00 .0 100.0 .406 -1.834 180 3 1 . 19 1.1 98.7 .487 -1. 583 210 15 5.99 7.1 92.7 .5 68 -1.332 240 23 9. 19 16.3 83.6 .649 -1.081 270 17 6.79 23.1 76.8 .731 -.830 3 00 19 7.59 30. 7 69.2 .812 -.579 330 25 9.99 40. 7 59.2 .893 -. 328 360 18 7. 19 47.9 52.0 .974 -. 077 390 26 10.39 58.3 41 .6 1 .056 .172 420 27 10.79 69. 1 30.8 1.137 . 423 450 18 7. 19 76.3 23.6 1.218 .674 480 16 6.39 82. 7 17.2 1.299 .925 510 15 5.99 8 8.7 11.2 1 .3 80 1. 176 540 8 3. 19 91, 9 8.0 1.462 1.427 5 70 6 2.39 94.3 5.6 1.543 1.678 600 8 3. 19 97.5 2.4 1.624 1.929 630 3 1. 19 98.7 1.2 1 .705 2.180 660 0 .00 98.7 1.2 1.787 2.431 690 0 . 00 98.7 1.2 1 .868 2.682 720 0 .00 98.7 1.2 1.949 2.933 750 0 .00 98.7 i...*.j2!.„. 2,030 , 3....1S3 780 1 .39 99.1 .8 2.112 3.434 810 1 .39 99.5 .4 2. 193 3. 685 840 0 .00 99.5 .4 2.274 3.9 36 870 0 .00 99. 5 .4 2.355 4.187 900 0 .00 99.5 .4 2.436 4. 438 930 1 . 39 100.0 .0 2.518 4.689 REMAINING FREQUENCIES ARE ALL ZERO

TABLE 212 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 250 927.515 262 .000 231879.000 NON-WEIGHTED

UPPER OBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVIATION LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 .00 .0 100.0 .032 -3.42 5 6 0 0 . 00 . 0 100.0 .064 -3.311 90 0 .00 .0 100.0 .097 -3.196 120 0 .00 .0 100.0 .129 -3.082 150 0 .00 .0 100.0 .161 -2.967 1 80 0 .00 .0 1 00.0 .194 -2.853 1 i- V \ 210 0 . 00 .0 100.0 .226 -2.73 8 240 0 .00 .0 100.0 .2 58 -2.624 270 0 .....o.o. ...0 .1.0.0 ...0 ...29 1 .. -2. 509 .. 3 00 1 . 39 .3 99 . 5 .323 -2.395 330 1 .39 .7 99. 1 .3 55 -2.280 360 1 .39 1 .1 98.7 .3 88 — 2.166 J 390 0 .00 1.1 98.7 .420 -2.051 420 0 .00 1. 1 98.7 .452 -1.93 7 450 4 1.59 2.7 97 . 1 .485 -1.822 480 3 1. 19 3.9 96. 0 .517 -1.708 510 3 1. 19 5.1 94.7 .549 -1. 593 540 3 1 .19 6. 3 93 .5 .582 -1.479 570 4 1.59 7.9 92.0 .614 -1. 364 600 7 2. 79 10.7 89. 1 .646 -1.250 630 13 5.19 15 .9 84.0 .679 -1.135 660 6 2 .39 18.3 81.6 .711 -1.021 690 3 1. 19 19.5 80.4 .743 -.906 720 15 5.99 25.5 74. 4 .776 -.792 750 9 3.59 29 . 1 7 0.8 .808 -.677 780 5 1.99 31.1 68.8 .840 -.563 810 5 1.99 33 . 1 66.8 .873 -.448 840 9 3.59 36.7 63.2 .905 -.334 870 8 3. 19 39 .9 60. 0 .937 -.219 900 16 6.39 46 .3 53.6 .970 -.105 930 11 4.39 50.7 49.2 1.002 .009 960 9 3.59 54.3 45.6 1.035 . 123 990 10 3.99 58.3 41.6 1.067 .238 1020 9 3.59 61.9 3 8.0 1.099 .352 1050 13 5. 19 67.1 32.8 1. 132 .467 1080 8 3. 19 70.3 29.6 1.164 ,582 1110 6 2.39 72. 7 27.2 1.196 .696 1140 15 5.99 78.7 21.2 1.229 .811 1170 9 3. 59 82 .3 17.6 1.26 1 .925 1200 7 2.79 85. 1 14, 8 1.293 1.040 12.30 8 3.19 88 .3 11 .6 1.326 1. 154 1260 3 1.19 89. 5 1 0.4 1 .358 1 .269 1290 6 2.39 91.9 8.0 1.390 1.3 83 1320 0 .00 91.9 8.0 1.423 1. 498 1350 6 2.39 94, 3 5.6 1.4.55 1.612 1380 1 .39 94.7 5.2 1.487 1.727 1410 3 1. 19 95.9 4.0 1.520 1.841 1440 4 1 .59 97. 5 2.4 1.552 1.956 1470 3 1. 19 98.7 1.2 1.5 84 2. 070 1500 0 . 00 98. 7 1.2 1 .6 17 2. 185 1530 1 .39 99. 1 .8 1.649 2.299 1560 2 . 79 100.0 .0 1.681 2.414 REMAINING FREQUENCIES ARE ALL ZERO

FABLE 312 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 2 50 1296. 831 237.375 324208.000 NGN-WEIGHTED

UPPER GBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVIATION L IM IT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 .00 .0 100.0 .023 -5.336 60 0 .00 .0 100.0 .046 -5.210 90 0 .00 .0 1 00. 0 .069 -5.084 120 0 .00 .0 100.0 .092 -4.957 150 0 . 00 .0 100 .0 .115 -4.831 lib 1 80 0 .00 .0 100.0 .138 -4.704 210 0 . 00 .0 100.0 .161 -4.578 240 0 .00 .0 1.0Q.0 ,185 -4,4.52 270 0 .00 .0 100.0 .208 -4.325 3 00 0 . 00 . 0 100.0 .231 -4.199 330 0 .00 .0 100.0 .254 -4.073 360 0 .00 .0 100 .0 .277 -3.946 390 0 .00 . 0 100.0 .300 -3.820 420 0 .00 .0 100.0 .3.23 -,3.....69.3. 450 0 . 00 .0 100.0 .346 -3.567 - 480 0 .00 .0 100.0 .370 -3.441 510 0 .00 .0 LOO.O . 393 -3,314 540 0 . 00 .0 100.0 .416 -3.188 570 0 .00 .0 100. 0 .439 -3.061 600 0 .00 .0 100.0 .462 -2.935 630 0 .00 .0 100.0 .485 -2.809 660 0 .00 .0 100.0 .508 -2.682 690 0 .00 .0 100.0 .532 -2. 556 720 0 .00 . 0 100.0 .555 -2.43 0 750 0 .00 .0 100.0 .578 -2.303 780 0 . 00 .0 100.0 .601 -2.177 8.10 0 .00 .0 100.0 .624 -2.050 840 1 .39 .3 99 .5 .647 -1.924 870 3 1 .19 1. 5 98.3 .670 -1.798 900 10 3.99 5 .5 94.3 . 693 -1.671 93 0 5 1 .99 7.5 92.3 .717 -1.545 960 3 1. 19 8. 7 91.1 .740 -1.418 990 7 2.79 11.5 88.3 .763 -1.292 1020 10 3.99 15.5 8 4.3 .786 -1.166 1050 6 2.39 17.9 82.0 .8 09 -1.039 1080 7 2.79 20 .7 79.2 .832 -.913 11 10 12 4.79 25. 5 74.4 .8.55 -.787 1140 11 4.39 29.9 70.0 .....8..7.9 -,...6.6.0 1170 8 3. 19 33.1 66 .8 .902 -. 534 1200 9 3.59 36.7 63.2 .925 -.407 1230 8 3. 19 39.9 60.0 .948 — .281 1260 9 3.59 43.5 56 .4 .971 -.155 1290 9 3.59 47. 1 52 . 8 .994 -.028 1320 14 5. 59 52.7 47.2 1.017 ,.092 1350 11 4.39 57. 1 42 .8 1 .040 .223 13 80 9 3. 59 60.7 39.2 1.064 .350 1410 12 4. 79 65.5 34.4 1.087 ,476 1440 12 4.79 70.3 29.6 1.110 .603 1470 13 5.19 75.5 24.4 1. 133 . 729 1500 6 2.39 77.9 22.0 1,156 ,..8.5.5 1530 7 2.79 80.7 19. 2 1.179 .982 1560 9 3.59 84.3 15.6. 1.202 1. 108 1590 6 2.39 86. 7 13.2 1 .226 1.235 1620 6 2.39 89. 1 10.8 1.249 1.361 1650 4 1.59 90.7 9.2 1.272 1.487 1680 10 3.99 94. 7 5.2 1 .295 1.614 1710 7 2.79 97.5 2.4 1.318 1. 740 1740 2 . 79 98.3 1 .6 1.341 1. 866 1770 4 1. 59 100.0 .0 1.364 1.993 REMAINING FREQUENCIES ARE ALL ;ZER O

TABLE 412 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 250 1301.4 95 237.187 325374.000 NON-WEIGHTED - UPPER OBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVIATION L IM I T FREQUENCY OF T OT Al PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 .00 ,.Q 100,0 ,023 -5,360 60 0 .00 .0 100.0 .046 -5.234 90 0 . 00 .0 100.0 .069 -5.107 120 0 .00 .0 100. 0 .092 -4.981 < 150 0 .00 .0 100.0 .115 -4. 854 180 0 . 00 .0 100.0 .138 -4.728 210 0 .00 .0 100. 0 .161 -4.601 240 0 . 00 .0 100.0 .184 -4.475 270 0 .00 .0 100.0 .207 -4.348 300 0 .00 .0 100.0 .230 -4.222 3 30 0 . 00 .0 .100.0 .253 -4.095 360 0 .00 .0 100. 0 .2 76 -3.969 390 0 .00 .0 100.0 .299 -3.842 420 0 .00 .0 100.0 .322 -3. 716 450 0 .00 .0 100.0 .345 -3.589 480 0 .00 .0 100,0 .368 -3. 463 510 0 .00 . 0 100.0 .39 1 -3.337 540 0 .00 .0 100.0 .414 -3.210 570 0 . 00 . 0 100.0 .437 -3. 084 600 0 .00 .0 100. 0 .461 -2.957 63 0 0 .00 .0 100.0 .484 -2. 831 66 0 0 .00 .0 100.0 .507 -2.704 690 0 .00 .0 100. 0 .530 -2.578 720 0 .00 .0 .100.0 .553 -2.451 750 0 .00 .0 100.0 .576 -2.32 5 780 0 .00 .0 100.0 .599 -2.198 810 0 . 00 .0 100 .0 .622 -2.072 840 1 .39 .3 99. 5 .645 -1.945 870 3 1 .19 1.5 98.3 .668 -1.819 9 00 6 2.39 3.9 96.0 .691 -1.692 930 8 3. 19 7. 1 92.7 .714 -1.566 960 4 1 .59 8.7 91.1 .737 -1.439 990 7 2.79 11.5 88.3 .760 -1.313 1020 6 2.39 13.9 86 .0 .783 -1.186 1050 9 3,59 17.5 8 2.4 .806 -1.060 1080 6 2.39 19.9 80, 0 .829 -.933 1110 12 4.79 24.7 75.2 • 852 807 1140 13 5.19 29.9 70.0 .875 -.6 80 1170 7 2.79 32.7 67.2 . 8 98 -.554 1200 8 3.19 35 .9 64.0 .922 -.427 1230 9 3. 59 39.5 60.4 .945 -.301 1260 9 3.59 43.1 56.8 .968 -.174 1290 9 3.59 46.7 5 3.2 .991 -.048 1320 13 5.19 51 .9 48. 0 1.014 .078 1350 10 3.99 55.9 44.0 1.037 .204 1380 1 1 4.39 60.3 39.6 1 .060 .330 1410 11 4.39 64. 7 35.2 1.083 .457 1440 12 4.79 69.5 30,4 1.106 . 583 1470 15 5.99 75. 5 24.4 1,129 .710 1500 6 2.39 77.9 22.0 1.152 . 836 1530 6 2.39 80.3 19 .6 1.175 .963 1560 7 2.79 83. 1 16. 8 1.198 1.089 1590 9 3.59 86.7 13.2 1.221 1.216 1620 4 1 . 59 88.3 11.6 1.244 1.342 1650 6 2 .39 90.7 9.2 1.267 1.469 1680 10 3.99 94.7 5.2 1.290 1.595 17 10 3 1. 19 95.9 4.0 1.313 1.722 1740 6 2.39 98.3 1.6 1.336 1. 84 8 1770 4 1.59 100.0 .0 1.359 1.975 REMAINING FREQUENCIES ARE ALL ZERO

TABLE 512 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS \ 374 1309.168 229 .562 489629.000 NON-WEIGHTED

UPPER OBSERVED PER CENT CUMULATIVE „.£UMULAI.IY£ MULTIPLE DEVI AT ION LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 .00 .0 100.0 .022 -5. 572 60 0 . 00 .0 100.0 .045 -5.441 90 0 .00 .0 100.0 .068 -5.310 120 0 .00 .0 100.0 .091 -5. 180 150 0 .00 .0 100.0 .114 -5.049 180 0 .00 .0 100.0 . 137 -4.918 2 10 0 .00 .0 100.0 .160 -4.788 240 0 .00 .0 100.0 .183 -4.657 270 0 .00 .0 100.0 .206 -4.526 300 0 .00 . 0 100.0 .229 -4.396 3 30 0 .00 .0 100.0 .252 -4.265 360 0 . 00 .0 100.0 .274 -4.134 390 0 .00 .0 100.0 .2 97 -4.003 420 0 .00 .0 100.0 .320 -3. 873 450 0 . 00 .0 100.0 .343 -3.'742 480 0 .00 .0 100.0 .366 -3.611 510 0 .00 .0 100.0 .389 -3.481 540 0 .00 .0 100.0 .412 -3.350 570 0 .00 .0 100.0 .43 5 -3.219 600 0 .00 .0 100.0 .458 -3. 089 6 30 0 .00 .0 100.0 .481 -2.958 660 0 .00 .0 100.0 .504 -2. 827 690 0 . 00 .0 100.0 .527 -2.697 720 0 .00 .0 100.0 .549 -2.566 750 0 .00 .0 100.0 .5 72 -2.435 780 0 .00 . 0 100.0 .595 -2.305 810 0 .00 .0 100.0 .6 18 -2.174 840 2 .53 .5 99 .4 .6 41 -2.043 870 11 2.94 3.4 96.5 .664 -1.913 900 7 1.87 5.3 94.6 .687 -1. 782 930 4 1. 06 6.4 93.5 .710 -1.651 960 7 1.87 8.2 91.7 .733 -1 .521 990 11 2-. 94 11.2 88.7 .756 -1.390 1020 7 1.87 13. 1 86.8 .779 -1.259 1050 11 2 .94 16.0 83 .9 .802 -.1.12.8. 1080 11 2.94 18.9 81.0 .824 -. 998 1110 10 2.67 2.1. 6 78.3 .847 -.867 1140 12 3.20 24.8 75.1 .870 -. 736 1170 14 3 . 74 28.6 71.3 .893 -.606 1200 12 3.20 31.8 68. 1 .916 -.475 1230 14 3. 74 35.5 64.4 .939 -.344 1260 13 3.47 39.0 60.9 .962 -.214 1290 27 7.21 46.2 53.7 .985 -.08 3 j r 132 0 11 2. 94 49 .1 50.8 1.008 .047 1350 16 4.27 53.4 46.5 1 .031 .177 1380 20 5.34 58.8 41.1 1.054 .308 1410 16 4.27 63 .1 36.8 1.077 .439 1440 33 8.82 71.9 28.0 . 1.099 .569 1470 10 2.67 74.5 25.4 1.122 .700 1500 11 2.94 77.5 22.4 1 .145 .831 1530 12 3.20 80. 7 19.2 1.168 1560 .961 13 3.47 84.2 15.7 1.191 1590 1.092 3 .80 85.0 14.9 1.2 14 1.223 1620 17 4.54 89.5 10.4 1.237 1.354 1650 18 4.81 94.3 5.6 1.260 1.484 1680 5 1.33 9 5.7 4.2 1 .283 1.615 17 10 12 3,20 98.9 1,0 1 .306 1. 746 1740 4 1.06 100.0 .0 1 .329 1 .876 REMAINING FREQUENCIES ARE ALI ,ZER O

TABLE 612 ENIRItS IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 374 12 09.908 240.875 452506.000 NON-WEIGHTED

UPPER OBSERVED P ER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVI AT I ON LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 00 .0 100.0 .0 24 -4.898 60 0 .00 .0 100. 0 .049 -4.773 90 0 .00 .0 100.0 .0 74 -4.649 120 0 . 00 .0 100 .0 .099 -4.524 150 0 .00 .0 100. 0 .123 -4.400 180 0 .00 .0 100.0 .148 -4.275 210 0 .00 .0 100.0 .173 -4.151 240 0 .00 .0 100.0 . 198 -4.026 270 0 .00 .0 100,0 .2 23 -3.902 300 0 .00 . 0 100.0 .247 -3 .777 330 0 .00 .0 100.0 .272 -3. 652 3 60 0 . 00 .0 100.0 .297 -3.528 390 0 .00 .0 100.0 .322 -3.403 420 0 ,00 .0 100.0 .347 -3.279 450 0 . 00 . 0 100.0 .3 71 -3.154 480 0 .00 .0 100.0 .3 96 -3.030 510 0 .00 .0 100.0 .421 -2.905 540 0 .00 .0 100.0 .446 -2.781 570 0 .00 ,0 100.0 .471 -2.656 600 0 . 00 . 0 100 .0 .495 -2.532 6 30 0 .00 .0 100. 0 .52 0 -2.407 660 0 .00 .0 100.0 • 545 -2.282 690 0 .00 . 0 100.0 .570 -2.158 720 0 . 00 .0 100.0 .5 95 -2.033 750 0 .00 .0 100.0 .6 19 - 1. 909 780 0 .00 . 0 100.0 .644 -1.784 810 0 .00 .0 100.0 .6 69 - 1.660 840 8 2. 13 97.8 .694 -1.535 870 20 5.34 7.4 92. 5 .719 -1.411 900 19 5.08 12.5 87.4 .743 -1. 286 ' 930 5 1 . 33 13.9 86.0 .76 8 -1. 162 960 19 5.08 18.9 81.0 .793 -1.037 990 18 4.81 23 .7 76.2 .818 -.912 1020 11 2.94 26. 7 73.2 .843 -.788 1050 12 3.20 29.9 70.0 .867 -.663 1080 20 5.34 35.2 64.7 .892 -.539 1110 13 3.47 38.7 61.2 .917 -.414 1140 13 3.47 42.2 5 7.7 .942 -.290 1170 15 4.01 46.2 53 . 7 .967 -.165 1200 17 4.54 50.8 49 .1 .991 -. 041 12 30 14 3.74 54.5 45.4 1 .016 .083 12 6 0 12 3.20 57. 7 42 .2 1.041 .207 1290 19 5.08 62.8 3 7.1 1.066 .332 13 20 10 2.67 65.5 34.4 1 .090 .457 1350 17 4.54 70.0 29.9 1. 115 .581 1380 14 3.74 73.7 26.2 1.140 . 706 1410 13 3.47 77.2 22 . 7 1.165 .830 1440 19 5.08 82.3 17.6 1. 190 .9 55 1470 5 1.33 83.6 16 .3 1.2 14 1.079 1500 8 2.13 85. 8 14.1 1.239 1.204 1530 10 2.67 88.5 11.4 1.264 1.328 156 0 7 1.87 90. 3 9.6 1 .289 1.453 1590 2 .53 90.9 9 .0 1.314 1. 577 1620 11 2.94 93.8 6.1 1.338 1.702 16 50 9 2.40 96.2 3.7 1.363 1.82 7 1680 3 .80 97.0 2.9 1.388 1.951 1710 7 1. 87 98.9 1.0 1.413 2.076 1740 4 1.06 100.0 .0 1.43 8 2.200 REMAINING FREQUENCIES ARE ALL .ZER O

TABLE 712 ENTRIES IN TABLE MEAN ARGUMENT STANOARD DEVIATION SUM OF ARGUMENTS 250 13 2.983 201.250 33246.000 NON-WEIGHTED

UPPER OBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVIATION LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER •F MEAN FROM MEAN 30 147 58. 79 58.7 41 .2 .225 -.511 60 5 I .99 60.7 39.2 .451 -.362 90 5 1.99 62.7 37.2 .676 -.213 120 5 i .99 64. 7 35.2 .902 -.064 1 50 7 2.79 67. 5 32.4 1.127 .0 84 180 9 3. 59 71.1 28.8 1.3 53 .233 210 5 1.99 73. 1 26.8 1.5 79 .382 240 8 3. 19 76.3 23.6 1.804 .531 270 3 1.19 77.5 22.4 2.030 .680 3 00 4 1.59 79. 1 20.8 2.255 .829 330 6 2.3 9 81.5 18.4 2.481 . 978 360 5 1.99 83.5 16.4 2 .707 1.12 8 3 90 * 5 1 .99 85.5 14.4 2.932 1.2 77 420 6 2. 39 87.9 12.0 3.158 1.426 450 5 1.99 89.9 10.0 3.3 83 1.575 4 80 6 2.39 92.3 7.6 3.6 09 1. 724 510 3 1. 19 93.5 6 .4 3.835 1.873 540 2 .79 94. 3 5.6 4.060 2. 022 570 1 .39 94.7 5.2 4.286 2. 171 600 2 .79 95. 5 4.4 4.5 11 2.320 630 2 .79 96.3 3.6 4.737 2. 469 660 1 . 39 96.7 3.2 4.9 63 2.618 690 4 1.59 98.3 1.6 5.188 2.767 72 0 0 .00 98.3 1.6 5.414 2. 916 750 2 . 79 99. 1 .8 5 .6 39 3.065 780 0 .00 99.1 . 8 5.865 3.214 810 1 .39 99 .5 .4 6.090 3. 364 840 0 .00 99. 5 .4 6.316 3.513 870 1 .39 100.0 .0 6.542 3.662 REMAINING FREQUENCIES ARE ALL ZERO

TABLE 812 ENTRIES IN TABLE MEAN ARGUMENT STANDARD DEVIATION SUM OF ARGUMENTS 250 1271.567 236.562 317892.000 NON-WEIGHTED m UPPER OBSERVED PER CENT CUMULATIVE CUMULATIVE MULTIPLE DEVIATION LIMIT FREQUENCY OF TOTAL PERCENTAGE REMAINDER OF MEAN FROM MEAN 30 0 ...OQ ,0 100... o .023 -5.248 60 0 .00 .0 100.0 .047 -5.121 9 0 0 .00 . 0 100.0 .070 -4.994 120 0 .00 .0 100.0 .0 94 -4.867 150 0 .00 .0 100.0 .117 -4. 741 180 0 .00 .0 100.0 .141 -4.614 210 0 ,00 .0 100.0 .165 -4, 487 240 0 . 00 .0 100.0 .188 -4.360 2 70 0 .00 .0 100.0 .212 -4,233 300 0 .00 .0 100.0 .235 -4.107 33 0 0 .00 .0 100.0 .2 59 -3.980 360 0 .00 .0 100. 0 .2 83 -3.853 390 0 .00 .0 100.0 .306 -3.726 420 0 .00 .0 100.0 .3 30 -3.599 450 0 .00 .0 100.0 .353 -3.472 480 0 . 00 .0 100.0 .377 -3.346 5 10 0 .00 .0 100. 0 .401 -3.219 540 0 .00 .0 100.0 .424 -3.092 570 0 .00 .0 100.0 .448 -2,965 6 00 0 .00 .0 100.0 .471 -2.838 630 0 .00 .0 100.0 .495 -2.712 660 0 .00 . 0 100.0 .5 19 -2.585 690 0 .00 .0 100.0 .542 -2.458 720 0 . 00 .0 100.0 .566 -2.331 750 0 .00 .0 100. 0 ,589 -2,204 780 0 .00 .0 100,0 .613 -2.077 810 0 .00 .0 100.0 .637 -1.951 840 2 .79 .7 99. 1 .660 -1.824 870 11 4.39 5.1 94.7 .684 -1.697 900 5 1.99 7.1 92 .7 .707 -1.570 930 3 1.19 8.3 91.5 . 731 -1. 443 960 6 2. 39 10.7 89 .1 .754 -1.317 990 10 3 .99 14.7 85. 1 .778 -1.190 102 0 7 2.79 17.5 82 .4 .8 02 - 1.063 1050 9 3.59 21.1 78.8 .825 -.936 1080 9 3 .59 24.7 75.2 .849 -.8 09 1110 9 3...5.9 2.8... 3 . 71,6 .8 72 -.682 1140 9 3.59 31. 9 68.0 .896 -.556 1170 8 3. 19 35. 1 64.8 .920 -. 429 1200 10 3.99 39.1 60.8 .943 -.302 1230 10 3.99 43. 1 56.8 .967 -.175 12 60 9 3.59 46.7 53.2 .990 -.048 129 0 15 5.99 52.7 47.2 1.0 14 ,077 1320 7 2.79 55.5 44. 4 1.03 8 .204 1350 12 4. 79 60.3 39.6 1.061 .331 13 80 10 3.99 64.3 35.6 1.0 85 .458 1410 12 4.79 69. 1 30.8 1. 108 .5 85 1440 18 7. 19 76.3 23.6 1.132 .711 1470 4 1.59 77.9 22. 0 1 .156 .838 1500 8 3. 19 81. 1 18.8 1.179 .965 1530 8 3 . 19 84.3 15.6 1 .2 03 1.092 1560 6 2 .39 86.7 13.2 1.226 1.219 1590 2 .79 87.5 12.4 1 .250 1. 346 162 0 9 3. 59 91. 1 8.8 1.274 1.472 1650 9 3.59 94.7 5 . 2 1.297 1, 599 1680 3 1. 19 95,9 4.0 1.321 1.72 6 1710 7 2. 79 98. 7 1,2 1.344 1.853 1740 3 1.19 100 . 0 .0 1.368 1. 980

131

APPENDIX G: A QUICK USEE'S GUIDE

To introduce a flight to the model the following lines

must be modified:

lines 168-194 the number of tables desired must go here with the

correct gate number as label. E.G. 104 for table 1, gate 4.

Line 192: The "E2" must match with the number of flights {two here) .

Line 193: Each gate (flight) assigned has a particular arrival, connection distribution.

Lines 207-212: Each flight has a particular combination of

pleasure and business passengers.

Lines 268-274: Each flight is introduced with a section like this.

The program can now be run.