DOCSLIB.ORG

Italic Entries Indicatefigures. 319-320,320

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Italic Entries Indicatefigures. 319-320,320 Index A Analytic hierarchy process, 16-24, role of ORIMS, 29-30 17-18 Availability,31 A * algorithm, 1 absolute, relative measurement Averch-lohnson hypothesis, 31 Acceptance sampling, 1 structural information, 17 Accounting prices. 1 absolute measurement, 23t, 23-24, Accreditation, 1 24 B Active constraint, 1 applications in industry, Active set methods, 1 government, 24 Backward chaining, 33 Activity, 1 comments on costlbenefit analysis, Backward Kolmogorov equations, 33 Activity-analysis problem, 1 17-19 Backward-recurrence time, 33 Activity level, 1 decomposition of problem into Balance equations, 33 Acyclic network, 1 hierarchy, 21 Balking, 33 Adjacency requirements formulation, eigenvector solution for weights, Banking, 33-36 facilities layout, 210 consistency, 19-20, 20t banking, 33-36 Adjacent, 1 employee evaluation hierarchy, 24 portfolio diversification, 35-36 Adjacent extreme points, 1 examples, 21-24 portfolio immunization, 34-35 Advertising, 1-2, 1-3 fundamental scale, 17, 18, 18t pricing contingent cash flows, competition, 3 hierarchic synthesis, rank, 20-21 33-34 optimal advertising policy, 2-3 pairwise comparison matrix for BarChart, 37 sales-advertising relationship, 2 levell, 21t, 22 Barrier, distance functions, 37-39 Affiliated values bidding model, 4 random consistency index, 20t modified barrier functions, 37-38 Affine-scaling algorithm, 4 ranking alternatives, 23t modified interior distance Affine transformation, 4 ranking intensities, 23t functions, 38-39 Agency theory, 4 relative measurement, 21, 21-22t, Basic feasible solution, 40 Agriculture 21-24 Basic solution, 40 crop production problems at farm structural difference between Basic vector, 40, 41 level,429 linear, nonlinear network, 18 Basis,40 food industry and, 4-6 structuring hierarchy, 20 Basis inverse, 40 natural resources, 428-429 synthesis, 23t Batch shops, 41 regional planning problems, 429 three level hierarchy, 17 Battle modeling, 41-44 AHP, 7 Animation, 24 attrition laws, 42-43 AI, 7. See Artificial intelligence Anticycling rules, 24 classification, 41-42 Air Force operations analysis, 7-10 Antithetic random variates, 25 Bayes rule, 45 1970s,9 Application areas for ORIMS in Bayesian decision theory, 44-45 1980s,9 industry,297 subjective probability, 44-45 issues, 9-10 Applied probability, 25 Beale tableau, 47 postwar Air Force OA under AFR Apportionment, 496 Bender's decomposition method, 47 20-7,8-9 Apprenticeship, 507 Bidding models, 47-49 World War II Air Force OA, 8 Arc, 25 Big-M method, 50 Air pollution, control of, 194-195 Archimedean axiom, 25 Bilevellinear programming, 50 Airline industry, 10-12 Arima, 25 Bin-packing crew scheduling, 11-12 Arrival-point distribution, 25 one-demensional,51-52 fleet assignment, 11 Arrival process, 25 two-dimensional, 52-53 flight schedule planning, 10-11 Arrow diagram, 25 Binary variable, 50 yield management, 12 Artificial intelligence, 25-27, 319 Bipartite graph, 53 Algebraic modeling language for artificial neural networks, Birth-death process, 53 optimization, 12-15, 13-14 319-320,320 Bland's anticycling rules, 53 Algorithm,16 computational logic, 26-27 Blending problem, 54 Algorithmic complexity, 16 expertlknowledge-based systems, Block-angular system, 54 Allocation, health care systems, 274 319 Block pivoting, 54 Alternate optima, 16 Heuristicsearch,25-26 Block-triangular matrix, 54 Alternate paths, 16 logic programming, expert Bootstrapping, 54 Alternatives, ranking, analytic systems, 27 Bounded rationality, 54 hierarchy process, 23t Artificial variables, 28 Bounded variable, 54 Analyst's manual, documentation Assignment, of fleet, airline industry, Brachistochrone problem, 57-58 and,170 11 Branch,54 Analytic combat model, 16 Assignment problem, 28 Brownian motion, 54 Automation, 28-30 Brown's algorithm, search theory, 619 Italic entries indicatefigures. 742 Index BTRAN,55 Computer-based group decision Crime, 126--131 Buffer, 55 process, 270 ambiguity, 129-130 Bulk queues, 55 Computer models, fire models, homicide, 126 Burke's theorem, 55, 446 224-225 Criterion Busy period, 55 Computer science, OR, 103-105 cone, 131 Concave function, 106 space, 132 Condition number, 106 vector, 132 c Cone, 106 Critical activity, 132 Congestion stock, inventory Critical path, 132 Calculus of variations, 57-59 modeling, 312 Critical path method (CPM), 132 Call priorities, 59 Congestion system, 106 Crossover, 132 Candidate rules, 59 Conjugate gradient method, 106 CSC. See Cumulative sum chart Capacitated transportation problem, 59 Connected graph, 106 Cumulative sum chart, quality Capital budgeting, 59-62 Conservation of flow, 106 control, 544-546 CDF,62 Consistency, eigenvector solution for, Curse of dimensionality, 132 Center for Naval Analysis, 62-66 analytic hierarchy process, Customer distribution, 132 military buildup, 65 19-20,20t Cut, 132 World WarIl, 63 Constrained optimization problem, Cut set, 132 Certainty equivalent, 66 107 Cutting stock problem, 132-137 Certainty factor, 66 Constraint, 107 CV,137 Chain, 66 Constraint qualification, 107 Cybernetics, 137-142 Chance-constrained programming, 66 Construction applications, 107-108 ergonomics, 138-139 Chaos theory, 66 Continuing role for field analysis, system design, 141-142 Chapman-Kolmogorovequations, 66 222-223 Cycle, 142 Chinese postman problem, 67-69 Continuous-time Markov chain, 199 Cyclic queueing network, 142 cycles, 67-69 Control Cyclic service discipline, 142 tours, 67-69 charts, 109 Cycling, 143 Choice strategies, 69 theroy, 109-113 Choice thoery, 72-72 Controllable variables, 109 Chromatic number, 72 Convex combination, 113 D Chromosome, 72 Convex cone, 113 CIM,72 Convex function, 113 Danzig-Wolfe decomposition Circling, 72 Convex hull, 113 algorithm, 145 Classical optimization, 72 Convex polyhedron, 113 Data envelopment analysis, 145-149 Closed network, 72 Convex-programming problem, 113 Farrell measure, 146 Cluster analysis, 72-74 Convex set, 113 ratio form model, 147-148 Cobb-Douglas production function, 75 Convexity rows, 113 Database design, 145 COEA,75 Cooperative games, 244, 244-245 DEA,150 Coefficent variation, 75 Corner point, 113 Decision analysis, 150-154 Cognitive mapping, 75 Corporate strategy, 114-118 Decision maker (DM), 155 Coherent system, 75 Cost Decision making, 155 Column generation, 76 allocation game, 244 Decision problem, 156 Column vector, 76 analysis, 119-121 Decision support system, 156--158 Combat analytic hierarchy process, 17-19 Decision trees, 159-160 model,76 coefficient, 122 Decision variables, 161 simulation, 76 effectiveness analysis, 122-125 Decomposition Combinatorial, integer optimization, evolution, 119-120 algorithm, 161 76-83 game, solutions to, 244 analytic hierarchy process, 21 Combinatorial explosion, 83 methods, 120-121 Degeneracy, 161 Combinatorics, 83 range, 125 Degenerate solution, 161 Common random variates, 85 row, 125 Degree, 161 Common value bidding model, 85 slope, 125 Delaunay triangulation, 161 Communications networks, 86-91 vector, 125 Delay, 161 design, 88-91 COV,125 Delphi method, 161-163 modeling, 87-88 Covering problem, 125 Density, 163 Community OR, 91 facility location, 214 function, 163 Competition, advertising and, 3 Coxian distribution, 125 Departure process, 163 Complementarity condition, 91 CPM,I25 Descriptive model, 163 Complementary pivot algorithm, 92 CPP, 125 Design, control, 164 Complementary problems, 92-94 Cramer's rule, 125 Detailed balance equations, 164 Complementary slackness theorem, 95 Cranes, material handling, 378 Determinant, 164 Computational complexity, 95-98 Crash Deterministic model, 164 Computational geometry, 98-101 cost, 125 Developing countries, 164-165 Computational logic, artificial time, 125 Development tool, 166 intelligence, 26-27 Crew scheduling, 125 Devex pricing, 166 Computational probability, 103 airline industry, 11-12 Deviation variables, 166 logistics, 355-356 DFR,166 Index 743 Diameter, 166 common history, 178-179 ETA file, 196 Diet problem, 166 electric power systems, 185-187 ETA matrix, 196 Diffusion approximation, 166 emergency services, 188-190 ETA vector, 196 Diffusion process, 166 fuel inventory planning, 186 Ethics, 197-199 Digraph, 166 generation system expansion Euler tour, 199 Dijkstra's algorithm, 166 planning, 186 Eulerian, Hamiltonian cycles, 262 Directed graph, 167 optimal dispatch of generating Evaluation, 199 Direction of set, 167 units, 185-186 EVOP, 199 Directional derivative, 167 optimal generation system EWMA chart. See Exponentially Discrete-event stochastic systems, reliability, 185 weighted moving average chart 62(H)32 optimal maintenance scheduling, Ex ante forecasts, 199 elements of simulation model, generating units, 186 Exclusive-or node, 199 62(H)28, 627t OR, common interests, 180-182 Expected utility theory, 199 input data, 627t perspectives, OR, 179-180 Expert systems, 199-202 input distribution selection, 628 utility resource planning, 186-187 artificial intelligence, 27 model validation, 632 Edge, 184 expert system development, 202 output analysis, 629~31 Efficiency, 184 general nature of expert system, simulation programming Efficiency frontier, 184 200-201 languages,62~29 Efficient algorithm, 184 inference-engine, 201-202 variance reduction techniques, Efficient point, 184 Exploratory modeling, 203-204 631-632 Efficient solution, 184 Exponential arrivals, 205 Discrete-programming problem, 167 Eigenvalue, 184 Exponential-bounded algorithm, 205 Discrete-time Markov chain, 167 Eigenvector, 185 Exponential smoothing, 205-206 Dispatching, logistics, 356 Eigenvector solution, analytic Exponentially weighted
Load more

Recommended publications
  • Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems·
    1 Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems· Onno Boxmat, Ger Koolet & Zhen Liui t CW/, Amsterdam, The Netherlands t INRIA-Sophia Antipolis, France This paper aims to give an overview of solution methods for the performance analysis of parallel and distributed systems. After a brief review of some important general solution methods, we discuss key models of parallel and distributed systems, and optimization issues, from the viewpoint of solution methodology. 1 INTRODUCTION The purpose of this paper is to present a survey of queueing theoretic methods for the quantitative modeling and analysis of parallel and distributed systems. We discuss a number of queueing models that can be viewed as key models for the performance analysis and optimization of parallel and distributed systems. Most of these models are very simple, but display an essential feature of dis­ tributed processing. In their simplest form they allow an exact analysis. We explore the possibilities and limitations of existing solution methods for these key models, with the purpose of obtaining insight into the potential of these solution methods for more realistic complex quantitative models. As far as references is concerned, we have restricted ourselves in the text mainly to key references that make a methodological contribution, and to sur­ veys that give the reader further access to the literature; we apologize for any inadvertent omissions. The reader is referred to Gelenbe's book [65] for a general introduction to the area of multiprocessor performance modeling and analysis. Stochastic Petri nets provide another formalism for modeling and perfor­ mance analysis of discrete event systems.
    [Show full text]
  • EUROPEAN CONFERENCE on QUEUEING THEORY 2016 Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop
    EUROPEAN CONFERENCE ON QUEUEING THEORY 2016 Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop To cite this version: Urtzi Ayesta, Marko Boon, Balakrishna Prabhu, Rhonda Righter, Maaike Verloop. EUROPEAN CONFERENCE ON QUEUEING THEORY 2016. Jul 2016, Toulouse, France. 72p, 2016. hal- 01368218 HAL Id: hal-01368218 https://hal.archives-ouvertes.fr/hal-01368218 Submitted on 19 Sep 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. EUROPEAN CONFERENCE ON QUEUEING THEORY 2016 Toulouse July 18 – 20, 2016 Booklet edited by Urtzi Ayesta LAAS-CNRS, France Marko Boon Eindhoven University of Technology, The Netherlands‘ Balakrishna Prabhu LAAS-CNRS, France Rhonda Righter UC Berkeley, USA Maaike Verloop IRIT-CNRS, France 2 Contents 1 Welcome Address 4 2 Organization 5 3 Sponsors 7 4 Program at a Glance 8 5 Plenaries 11 6 Takács Award 13 7 Social Events 15 8 Sessions 16 9 Abstracts 24 10 Author Index 71 3 1 Welcome Address Dear Participant, It is our pleasure to welcome you to the second edition of the European Conference on Queueing Theory (ECQT) to be held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT in Toulouse.
    [Show full text]
  • Delay Models in Data Networks
    3 Delay Models in Data Networks 3.1 INTRODUCTION One of the most important perfonnance measures of a data network is the average delay required to deliver a packet from origin to destination. Furthennore, delay considerations strongly influence the choice and perfonnance of network algorithms, such as routing and flow control. For these reasons, it is important to understand the nature and mechanism of delay, and the manner in which it depends on the characteristics of the network. Queueing theory is the primary methodological framework for analyzing network delay. Its use often requires simplifying assumptions since, unfortunately, more real- istic assumptions make meaningful analysis extremely difficult. For this reason, it is sometimes impossible to obtain accurate quantitative delay predictions on the basis of queueing models. Nevertheless, these models often provide a basis for adequate delay approximations, as well as valuable qualitative results and worthwhile insights. In what follows, we will mostly focus on packet delay within the communication subnet (i.e., the network layer). This delay is the sum of delays on each subnet link traversed by the packet. Each link delay in tum consists of four components. 149 150 Delay Models in Data Networks Chap. 3 1. The processinR delay between the time the packet is correctly received at the head node of the link and the time the packet is assigned to an outgoing link queue for transmission. (In some systems, we must add to this delay some additional processing time at the DLC and physical layers.) 2. The queueinR delay between the time the packet is assigned to a queue for trans- mission and the time it starts being transmitted.
    [Show full text]
  • Approximating Heavy Traffic with Brownian Motion
    APPROXIMATING HEAVY TRAFFIC WITH BROWNIAN MOTION HARVEY BARNHARD Abstract. This expository paper shows how waiting times of certain queuing systems can be approximated by Brownian motion. In particular, when cus- tomers exit a queue at a slightly faster rate than they enter, the waiting time of the nth customer can be approximated by the supremum of reflected Brow- nian motion with negative drift. Along the way, we introduce fundamental concepts of queuing theory and Brownian motion. This paper assumes some familiarity of stochastic processes. Contents 1. Introduction 2 2. Queuing Theory 2 2.1. Continuous-Time Markov Chains 3 2.2. Birth-Death Processes 5 2.3. Queuing Models 6 2.4. Properties of Queuing Models 7 3. Brownian Motion 8 3.1. Random Walks 9 3.2. Brownian Motion, Definition 10 3.3. Brownian Motion, Construction 11 3.3.1. Scaled Random Walks 11 3.3.2. Skorokhod Embedding 12 3.3.3. Strong Approximation 14 3.4. Brownian Motion, Variants 15 4. Heavy Traffic Approximation 17 Acknowledgements 19 References 20 Date: November 21, 2018. 1 2 HARVEY BARNHARD 1. Introduction This paper discusses a Brownian Motion approximation of waiting times in sto- chastic systems known as queues. The paper begins by covering the fundamental concepts of queuing theory, the mathematical study of waiting lines. After waiting times and heavy traffic are introduced, we define and construct Brownian motion. The construction of Brownian motion in this paper will be less thorough than other texts on the subject. We instead emphasize the components of the construction most relevant to the final result—the waiting time of customers in a simple queue can be approximated with Brownian motion.
    [Show full text]
  • Optimal Surplus Capacity Utilization in Polling Systems Via Fluid Models
    Optimal Surplus Capacity Utilization in Polling Systems via Fluid Models Ayush Rawal, Veeraruna Kavitha and Manu K. Gupta Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai - 400076, India E-mail: ayush.rawal, vkavitha, manu.gupta @iitb.ac.in Abstract—We discuss the idea of differential fairness in polling while maintaining the QoS requirements of primary customers. systems. One such example scenario is: primary customers One can model this scenario with polling systems, when it demand certain Quality of Service (QoS) and the idea is to takes non-zero time to switch the services between the two utilize the surplus server capacity to serve a secondary class of customers. We use achievable region approach for this. Towards classes of customers. Another example scenario is that of data this, we consider a two queue polling system and study its and voice users utilizing the same wireless network. In this ‘approximate achievable region’ using a new class of delay case, the network needs to maintain the drop probability of priority kind of schedulers. We obtain this approximate region, the impatient voice customers (who drop calls if not picked-up via a limit polling system with fluid queues. The approximation is within a negligible waiting times) below an acceptable level. accurate in the limit when the arrival rates and the service rates converge towards infinity while maintaining the load factor and Alongside, it also needs to optimize the expected sojourn times the ratio of arrival rates fixed. We show that the set of proposed of the data calls. schedulers and the exhaustive schedulers form a complete class: Polling systems can be categorized on the basis of different every point in the region is achieved by one of those schedulers.
    [Show full text]
  • Queueing Systems
    Queueing Systems Ivo Adan and Jacques Resing Department of Mathematics and Computing Science Eindhoven University of Technology P.O. Box 513, 5600 MB Eindhoven, The Netherlands March 26, 2015 Contents 1 Introduction7 1.1 Examples....................................7 2 Basic concepts from probability theory 11 2.1 Random variable................................ 11 2.2 Generating function............................... 11 2.3 Laplace-Stieltjes transform........................... 12 2.4 Useful probability distributions........................ 12 2.4.1 Geometric distribution......................... 12 2.4.2 Poisson distribution........................... 13 2.4.3 Exponential distribution........................ 13 2.4.4 Erlang distribution........................... 14 2.4.5 Hyperexponential distribution..................... 15 2.4.6 Phase-type distribution......................... 16 2.5 Fitting distributions.............................. 17 2.6 Poisson process................................. 18 2.7 Exercises..................................... 20 3 Queueing models and some fundamental relations 23 3.1 Queueing models and Kendall's notation................... 23 3.2 Occupation rate................................. 25 3.3 Performance measures............................. 25 3.4 Little's law................................... 26 3.5 PASTA property................................ 27 3.6 Exercises..................................... 28 4 M=M=1 queue 29 4.1 Time-dependent behaviour........................... 29 4.2 Limiting behavior...............................
    [Show full text]
  • A Survey on Performance Analysis of Warehouse Carousel Systems
    A Survey on Performance Analysis of Warehouse Carousel Systems N. Litvak∗, M. Vlasiou∗∗ August 6, 2018 ∗ Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Applied Mathematics, University of Twente, 7500 AE Enschede, The Netherlands. ∗∗ Eurandom and Department of Mathematics & Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. [email protected], [email protected] Abstract This paper gives an overview of recent research on the performance evaluation and design of carousel systems. We discuss picking strategies for problems involving one carousel, consider the throughput of the system for problems involving two carousels, give an overview of related problems in this area, and present an extensive literature review. Emphasis has been given on future research directions in this area. Keywords: order picking, carousels systems, travel time, throughput AMS Subject Classification: 90B05, 90B15 1 Introduction A carousel is an automated storage and retrieval system, widely used in modern warehouses. It consists of a number of shelves or drawers, which are linked together and are rotating in a closed loop. It is operated by a picker (human or robotic) that has a fixed position in front of the carousel. A typical vertical carousel is given in Figure1. arXiv:1404.5591v1 [math.PR] 22 Apr 2014 Carousels are widely used for storage and retrieval of small and medium-sized items, such as health and beauty products, repair parts of boilers for space heating, parts of vacuum cleaners and sewing machines, books, shoes and many other goods. In e-commerce companies use carousel to store small items and manage small individual orders.
    [Show full text]
  • Queueing-Theoretic End-To-End Latency Modeling of Future Wireless Networks Schulz
    Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Schulz Technische Universität Dresden Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Philipp Schulz der Fakultät Elektrotechnik und Informationstechnik der Technischen Universität Dresden zur Erlangung des akademischen Grades Doktoringenieur (Dr.-Ing.) genehmigte Dissertation Vorsitzender: Prof. Dr.-Ing. habil. Leon Urbas Gutachter: Prof. Dr.-Ing. Dr. h.c. Gerhard Fettweis Univ. Prof. Dr. techn. Markus Rupp Tag der Einreichung: 01. Oktober 2019 Tag der Verteidigung: 13. Januar 2020 Philipp Schulz Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks Dissertation, 13. Januar 2020 Vodafone Chair Mobile Communications Systems Institut für Nachrichtentechnik Fakultät Elektrotechnik und Informationstechnik Technische Universität Dresden 01062 Dresden Abstract The fifth generation (5G) of mobile communication networks is envisioned to enable a variety of novel applications. These applications demand requirements from the network, which are diverse and challenging. Consequently, the mobile network has to be not only capable to meet the demands of one of these appli- cations, but also be flexible enough that it can be tailored to different needs of various services. Among these new applications, there are use cases that require low latency as well as an ultra-high reliability, e. g., to ensure unobstructed pro- duction in factory automation or road safety for (autonomous) transportation. In these domains, the requirements are crucial, since violating them may lead to financial or even human damage. Hence, an ultra-low probability of failure is necessary. Based on this, two major questions arise that are the motivation for this thesis. First, how can ultra-low failure probabilities be evaluated, since experiments or simulations would require a tremendous number of runs and, thus, turn out to be infeasible.
    [Show full text]
  • Applications of Markov Decision Processes in Communication Networks: a Survey
    Applications of Markov Decision Processes in Communication Networks: a Survey Eitan Altman∗ Abstract We present in this Chapter a survey on applications of MDPs to com- munication networks. We survey both the different applications areas in communication networks as well as the theoretical tools that have been developed to model and to solve the resulting control problems. 1 Introduction Various traditional communication networks have long coexisted providing dis- joint specific services: telephony, data networks and cable TV. Their operation has involved decision making that can be modelled within the stochastic control framework. Their decisions include the choice of routes (for example, if a direct route is not available then a decision has to be taken which alternative route can be taken) and call admission control; if a direct route is not available, it might be wise at some situations not to admit a call even if some alternative route exists. In contrast to these traditional networks, dedicated to a single application, today’s networks are designed to integrate heterogeneous traffic types (voice, video, data) into one single network. As a result, new challenging control prob- lems arise, such as congestion and flow control and dynamic bandwidth alloca- tion. Moreover, control problems that had already appeared in traditional net- works reappear here with a higher complexity. For example, calls corresponding to different applications require typically different amount of network resources (e.g., bandwidth) and different performance bounds (delays, loss probabilities, throughputs). Admission control then becomes much more complex than it was in telephony, in which all calls required the same performance characteristics and the same type of resources (same throughput, bounds on loss rates and on delay variation).
    [Show full text]
  • Lindley-Type Recursions THOMAS STIELTJES INSTITUTE for MATHEMATICS
    Lindley-type recursions THOMAS STIELTJES INSTITUTE FOR MATHEMATICS CIP-DATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN c Vlasiou, Maria Lindley-type recursions / by Maria Vlasiou. – Eindhoven: Technische Universiteit Eindhoven, 2006. Proefschrift. – ISBN 90-386-0784-9 – ISBN 978-90-386-0784-9 NUR 919 Subject headings: recurrence relations / queuing theory 2000 Mathematics Subject Classification: 60K20, 60K25, 90B22 Printed by PrintPartners Ipskamp Cover design by Rik Wisman Kindly supported by a full scholarship from the legacy of L. Athanasoula Lindley-type recursions proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de Rector Magnificus, prof.dr.ir. C.J. van Duijn, voor een commissie aangewezen door het College voor Promoties in het openbaar te verdedigen op maandag 25 september 2006 om 16.00 uur door Maria Vlasiou geboren te Drama, Griekenland Dit proefschrift is goedgekeurd door de promotoren: prof.dr. J. Wessels en prof.dr.ir. O.J. Boxma Copromotor: dr.ir. I.J.B.F. Adan Acknowledgements The book is finally over and I feel grateful for the faith many people have shown in me throughout all the years I have been studying. This manuscript is the result of a few years of training under the guidance of my supervisor, Ivo Adan. I thank him for his endless patience in his interaction with me, for the fact that he always made time for me and that he constantly stimulated me to keep on learning. Onno Boxma was always available to discuss anything that I would come up with, no matter if it would be the problem that I was stuck with for the past few months, or my opinion on yesterday’s tennis game.
    [Show full text]
  • Mathematical Modelling of End-To-End Packet Delay in Multi
    Mathematical Modelling of End-to-End Packet Delay in Multi-hop Wireless Networks and their Applications to QoS Provisioning A thesis submitted for the degree of Doctor of Philosophy by Yu Chen Communications and Information Systems Research Group Department of Electronic and Electrical Engineering University College London November 2013 Statement of Originality I, Yu Chen confirm that the work presented in this thesis is my own. Where informa- tion has been derived from other sources, I confirm that this has been indicated in the thesis. Signed: Date: 2 “Time is money.” — Benjamin Franklin (1706 – 1790) “Je n’ai pas le temps.” (I do not have time.) — Evariste´ Galois (1811 – 1832) 3 Abstract This thesis addresses the mathematical modelling of end-to-end packet delay for Quality of Service (QoS) provisioning in multi-hop wireless networks. The multi-hop wireless technology increases capacity and coverage in a cost-effective way and it has been standardised in the Fourth-Generation (4G) standards. The effective capacity model approximates end-to-end delay performances, including Com- plementary Cumulative Density Function (CCDF) of delay, average delay and jitter. This model is first tested using Internet traffic trace from a real gigabit Ethernet gateway. The effective capacity model is developed based on single-hop and continuous-time com- munication systems but a multi-hop wireless system is better described to be multi-hop and time-slotted. The thesis extends the effective capacity model by taking multi-hop and time- slotted concepts into account, resulting in two new mathematical models: the multi-hop effec- tive capacity model for multi-hop networks and the Mixed Continuous/Discrete-Time (MCDT) effective capacity model for time-slotted networks.
    [Show full text]
  • Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems
    Queueing-Theoretic Solution Methods for Models of Parallel and Distributed Systems Onno Boxmay, Ger Kooley & Zhen Liuz yCWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands zINRIA Sophia-Antipolis, B.P. 93, 06902 Sophia-Antipolis, France Abstract This paper aims to give an overview of solution methods for the performance analysis of parallel and distributed systems. After a brief review of some important general solution methods, we discuss key models of parallel and distributed systems, and optimization issues, from the viewpoint of solution methodology. AMS Subject Classification (1991): 60K20, 68M20 Keywords & Phrases: queueing, performance evaluation, parallel systems Note: Supported by the European Grant BRA-QMIPS of CEC DG XIII, the second author is also supported by NFI. This paper appeared also in Performance Evaluation of Parallel and Distributed Systems | Solution Methods, O.J. Boxma and G.M. Koole (eds.), CWI, Amsterdam, 1994 (CWI Tract 105 & 106). 1. Introduction The purpose of this paper is to present a survey of queueing theoretic methods for the quantitative modeling and analysis of parallel and distributed systems. We discuss a number of queueing models that can be viewed as key models for the performance analysis and optimization of parallel and distributed systems. Most of these models are very simple, but display an essential feature of distributed processing. In their simplest form they allow an exact analysis. We explore the possibilities and limitations of existing solution methods for these key models, with the purpose of obtaining insight into the potential of these solution methods for more realistic complex quantitative models. As far as references is concerned, we have restricted ourselves in the text mainly to key references that make a methodological contribution, and to surveys that give the reader further access to the literature; we apologize for any inadvertent omissions.
    [Show full text]