International Series in Operations Research & Management ...978-1-4614-6777-9/1.pdf · International Series in Operations Research & Management Science Volume 192 Series Editor Frederick

International Series in OperationsResearch & Management Science

Volume 192

Series EditorFrederick S. HillierStanford University, CA, USA

Special Editorial ConsultantCamille C. PriceStephen F. Austin State University, TX, USA

For further volumes:http://www.springer.com/series/6161

J. MacGregor Smith • Barıs TanEditors

Handbook of StochasticModels and Analysisof Manufacturing SystemOperations

123

EditorsJ. MacGregor SmithMIE DepartmentUniversity of Massachusetts AmherstAmherst, MassachusettsUSA

Barıs TanCollege of Administrative Sciences

and EconomicsKoc UniversitySarıyer, Istanbul, Turkey

ISSN 0884-8289ISBN 978-1-4614-6776-2 ISBN 978-1-4614-6777-9 (eBook)DOI 10.1007/978-1-4614-6777-9Springer New York Heidelberg Dordrecht London

Library of Congress Control Number: 2013933704

© Springer Science+Business Media New York 2013This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part ofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting, reproduction on microfilms or in any other physical way, and transmission or informationstorage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodologynow known or hereafter developed. Exempted from this legal reservation are brief excerpts in connectionwith reviews or scholarly analysis or material supplied specifically for the purpose of being enteredand executed on a computer system, for exclusive use by the purchaser of the work. Duplication ofthis publication or parts thereof is permitted only under the provisions of the Copyright Law of thePublisher’s location, in its current version, and permission for use must always be obtained from Springer.Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violationsare liable to prosecution under the respective Copyright Law.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.While the advice and information in this book are believed to be true and accurate at the date of pub-lication, neither the authors nor the editors nor the publisher can accept any legal responsibility for anyerrors or omissions that may be made. The publisher makes no warranty, express or implied, with respectto the material contained herein.

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This volume is dedicated to ProfessorChristos T. Papadopoulos, the founder of theconference series which has led to theformation of the Stochastic Models ofManufacturing and Service Operations(SMMSO) of which the contributors of thisvolume are members. His tireless energy andenthusiasm for this research area has giveneveryone involved a shining example tofollow. We trust that the contributions withinthe volume live up to his exacting standards.

“Do not say a little in many words but agreat deal in a few” –Pythagoras.

Preface

The origins and history of the research work underlying this volume stem from a se-ries of conferences which began in 1997 on the Island of Samos, Greece. In May ofthat year, Professor Christos Papadopoulos convened thirty-five research scientistsand practitioners from all over the world to share their knowledge of the applicationof stochastic modeling and processes in manufacturing systems. Figure 1 shows theparticipants from the first conference

His organization and skill at managing the first and subsequent conferences in-spired us all to follow him in his quest to encapsulate and extend the work to what it

Fig. 1 Participants in Samos, Greece at the 1st meeting

vii

viii Preface

Fig. 2 List of participants at the 1st meeting

has become in the present day. Christos is seated in the center of the photo, identifiedas #23 in Fig. 2.

Since 1997, there have been eight workshop/conferences every 2 years contin-uing the important seminal efforts of Christos. The conference/workshops held todate include:

• First Conference, Performance Evaluation and Optimization of Production Lines,Samos Greece, May 19–22, 1997

• Second Conference, Analysis and Modeling of Manufacturing Systems, TinosIsland, Greece, May 16–20, 1999

• Third Conference, Design and Analysis of Manufacturing Systems, Tinos Island,Greece, May 19–22, 2001

• Fourth Conference, Analysis of Manufacturing Systems, Samos Island, Greece,July 1–4, 2003

• Fifth Conference, Analysis of Manufacturing Systems- Production Management,Zakynthos Island, Greece, May 20–25, 2005

Preface ix

• Sixth Conference, Analysis of Manufacturing Systems, Luntern, The Nether-lands, May 11–16 2007

• Seventh Conference, Stochastic Models of Manufacturing and Service Opera-tions, Ostuni, Italy, June 7–12, 2009

• Eighth Conference, Stochastic Models of Manufacturing and Service Operations,Kusadasi, Turkey, May 28-June 02, 2011

During the seventh workshop in the Netherlands, the scientific committee arrivedat the acronym Stochastic Models of Manufacturing and Service Operations(SMMSO) as a characterization of the research topics covered by the workshop.The IX th conference is scheduled to be held in Germany at the end of May 2013.

While there is some overlap and natural integration, the following nine categoriesof research issues and concerns have emerged as the academic discipline of theconferences:

• [Performance Analysis (PA)]: Decomposition, queueing theory, Markov pro-cesses, exact and heuristic methods and simulation.

• [Production Systems (PS)]: Flow, transfer, and Bernoulli lines, material handlingsystems, open, closed, and mixed queueing network models.

• [Supply Chains (SC)]: Bullwhip effect, cross-docking, transportation systems.• [Production and Inventory Control (IC)]: Part-release mechanisms, make-to-

order/make-to-stock, push/pull systems, base stock, lean manufacturing, leadtimes, lot sizing.

• [Quality Control (QC)]: Inspection stations, defects, machine failures, feedback.• [Energy and Environment (EE)]: Sustainability, recycling, and waste manage-

ment.• [Optimization (OP)]: Buffers, servers, workload allocation, and routing.• [Sequencing and Scheduling (SS)]: Job shops, open shops, admission control,

release dates.• [Engineering Economy and Finance (EF)]: Evaluating alternatives, amortization,

cost analysis, and cost savings.

Figure 3 graphically depicts the subject matter discussed at most all of theSMMSO conferences and while the theory of stochastic processes, optimization,and production-inventory systems remain the academic underpinning for all the top-ics, it is the unique way in which the participants interact with these topics that haslaid the foundation for this volume.

We conceived the idea of this volume as a way to document the foundationsand academic principles of the SMMSO philosophy. This volume is designed tobe a tutorial introduction to many of the research topics and issues encompassedby SMMSO. Thus, the intended audience of this volume are those people fromacademia and the practicing world who deal with all aspects of stochastic modelingin manufacturing and service systems.

The topics included in the various chapters are ordered as depicted in Fig. 4 andsummarized below:

x Preface

Perf.Anal.

Production Systems

Supply Chain

Prod.Inv.Cont.

Quality

Energ.Env.

OptimizationSeq.Scheduling

Econ.Fin.

SMMSO

Fig. 3 Subject matter of the SMMSO conferences

Ch. 4

Ch. 11

Ch. 3

Ch. 2

Ch. 8

Ch. 7

Ch. 5

Ch. 9

Ch. 10

Ch. 6

IC,PA,PS,SC,SS

PA,PS,QC,IC,SS

PA,PS,OP,SC,EF

Ch.1

Fig. 4 Organization of the chapters in the volume

• Chapter 1: John A. Buzacott, The Design of Manufacturing Systems to Copewith Variability. The first chapter is by the founder of Stochastic modelling ofmanufacturing systems. A comprehensive, insightful overview of the impact ofvariability in stochastic modeling on all aspects of manufacturing systems fromjob shops to flexible manufacturing systems. A must reading to start.

• Chapter 2: Xiao Cai, Sunderesh S. Heragu and Yang Liu, Modeling Auto-mated Warehouses Using Semi-Open Queueing Networks. A comprehensive and

Preface xi

Fig. 5 Participants in Ephesus, Turkey at the 8th SMMSO meeting

informative overview of semi-open queueing networks and their impact on thedesign and operations of automated warehousing.

• Chapter 3: P. Fernandes, M.E.J. O’Kelly, C.T. Papadopoulos and A. Sales, ExactAnalysis of Discrete Part Production Lines: The Markovian Queueing Networkand the Stochastic Automata Networks Formalisms. A comprehensive overviewof exact methods to evaluate the performance of discrete part production linesand an informative introduction of the Stochastic Automata Networks for theanalysis of production systems.

• Chapter 4: Kai Furmans and Martin Veit, Models of Leveling for Lean Manu-facturing Systems. An introduction to stochastic models for lean manufacturingsystems with a focus on practical methods to evaluate heijunka levelling in leanproduction systems.

• Chapter 5: Fikri Karaesmen, Value of Advance Demand Information in Produc-tion and Inventory Systems with Shared Resources. An insightful and informativesurvey of the methodology and approaches of advance design information in pro-duction and inventory systems.

xii Preface

Fig. 6 Tayfur Altiok

• Chapter 6: Jingshan Li, Semyon M. Meerkov, and Liang Zhang, Production Sys-tems Engineering: Review and Recent Developments. A tour de force of the fieldof Production Systems and its impact on manufacturing systems.

• Chapter 7: George Liberopolos, Production Release Control: Paced, WIP-Basedor Demand-Driven? Revisiting the Push/Pull and Make-to-Order/Make-to-StockDistinctions. A thorough discussion of push/pull and make-to-order/make-to-stock classifications of various production control mechanisms and precise defi-nitions of these commonly used terms.

• Chapter 8: J. MacGregor Smith, Queueing Network Models of Material Handlingand Transportation Systems. A detailed introduction and discussion of topologi-cal network design of transportation systems and presentation of various methodsto analyze series, merge, and split topologies by using state dependent queues.

• Chapter 9: B. Tan, Analysis of Output Variability. A complementary chapter toBuzacott’s presenting an overview of the methods to obtain performance mea-sures related to the variability of the output from discrete-material flow produc-tion systems that are modelled as Markovian systems.

• Chapter 10: Horst Tempelmeier, Stochastic Lot Sizing Problems. A comprehen-sive overview of the issues and methods for stochastic lot sizing problems withrandom demands.

Preface xiii

• Chapter 11: Nico J. Vandaele, From Operational to Financial Evaluation of Man-ufacturing Systems. An instructive discussion of the need and the types of mod-elling approaches to link the operational performance evaluation to the financialevaluation of manufacturing systems.

The conference publications have not only spawned a number of new ideas, buthave resulted in journal publications in the Annals of Operations Research (AOR),OR Spectrum, IIE Transactions, and a Kluwer Special Volume.

The photo in Fig. 5 is from the most recent meeting in Ephesus, Turkey in 2011.One of the founding participants and the invited speaker at the VIII conference,Professor Tayfur Altiok from Rutgers University, recently passed away. Tayfur isthe fourth person to the right seated in the top leftmost row in Fig. 5 and also shownin Fig. 6. He was a vital member of the first conference and the last meeting. He willbe sorely missed.

We trust that you will find in this volume a valuable set of tutorials and funda-mentals of the various research topics of the SMMSO universe.

Amherst, MA, USA J. MacGregor SmithIstanbul, Turkey Barıs Tan

Contents

1 The Design of Manufacturing Systems to Cope with Variability . . . . 1John A. Buzacott1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Manufacturing Needs Dedicated Problem Solvers . . . . . . . . . . . . . . 21.3 Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Job Shops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3.2 Flow Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4 Improving on the Job Shop and the Flow Line . . . . . . . . . . . . . . . . . 161.4.1 Flexible Manufacturing Systems (FMS) . . . . . . . . . . . . . . . 171.4.2 Central Storage and Dispatch . . . . . . . . . . . . . . . . . . . . . . . . 201.4.3 Cells and Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2 Modeling Automated Warehouses Using Semi-Open QueueingNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Xiao Cai, Sunderesh S. Heragu, and Yang Liu2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.2 SOQN Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.3 Single-Class SOQN with Two Stages of Exponential Servers

and Poisson Arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.3.1 State Space Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.3.2 Matrix Geometric Method Solution . . . . . . . . . . . . . . . . . . 372.3.3 Numerical Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.4 Single-Class SOQN with Multiple Stages of Exponential Serversand Poisson Arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422.4.1 Decomposition-Aggregation Method . . . . . . . . . . . . . . . . . 422.4.2 Numerical Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.5 Phase-Type Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.5.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.5.2 Closure Properties and Kronecker Product . . . . . . . . . . . . . 47

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2.6 Single-Class SOQN with Two Stages of General Serversand General Arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.6.1 State Space Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.6.2 Numerical Example 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542.6.3 Multiple Servers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.6.4 Numerical Example 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

2.7 Single-Class SOQN with Multiple Stages of General Serversand General Arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602.7.1 Modified Decomposition-Aggregation Method . . . . . . . . . 602.7.2 Numerical Example 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.8 Multi-Class SOQN with Multiple Stages of General Serversand General Arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.8.1 Aggregation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.8.2 Numerical Example 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3 Exact Analysis of Discrete Part Production Lines: The MarkovianQueueing Network and the Stochastic Automata NetworksFormalisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73P. Fernandes, M.E.J. O’Kelly, C.T. Papadopoulos, and A. Sales3.1 Introduction and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . 733.2 The Markovian Formalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.2.1 The Algorithm for the Generation of the ConservativeMatrix A for K-Station Reliable Exponential ProductionLines with Inter-station Buffers . . . . . . . . . . . . . . . . . . . . . . 80

3.2.2 The Queueing Network Model of a Three-StationReliable Exponential Production Line . . . . . . . . . . . . . . . . 87

3.3 The Stochastic Automata Networks Formalism (SAN) . . . . . . . . . . 903.3.1 Definitions and Properties of Classical Tensor Algebra

(CTA) and Generalized Tensor Algebra (GTA) . . . . . . . . . 913.3.2 Definition of Kronecker Descriptors Using Tensor

Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963.3.3 The Equivalent SAN Model to the Queueing Network

Model of the Three-Station Line . . . . . . . . . . . . . . . . . . . . . 973.4 Software Tools and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

3.4.1 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

4 Models of Leveling for Lean Manufacturing Systems . . . . . . . . . . . . . . 115Kai Furmans and Martin Veit4.1 Stochastic Models for Lean Manufacturing Systems . . . . . . . . . . . . 1154.2 System Description and Single Stage Model . . . . . . . . . . . . . . . . . . . 118

4.2.1 Performance Measure Calculation by Variable IntervalModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Contents xvii

4.2.2 Performance Measure Calculation by Fixed IntervalModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

4.3 Multi-stage Fixed Interval Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.3.1 Simplified Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1234.3.2 Iterative Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

4.4 An Application and Some Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . 1334.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

5 Value of Advance Demand Information in Productionand Inventory Systems with Shared Resources . . . . . . . . . . . . . . . . . . . 139Fikri Karaesmen5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

5.2.1 Modeling ADI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1405.2.2 Uncapacitated Inventory Systems . . . . . . . . . . . . . . . . . . . . 1425.2.3 Production/Inventory Systems . . . . . . . . . . . . . . . . . . . . . . . 143

5.3 Supply Chain Structures and Resource Sharing . . . . . . . . . . . . . . . . 1445.4 A Static Model: Newsvendor Framework . . . . . . . . . . . . . . . . . . . . . 145

5.4.1 No Inventory Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1465.5 Inventory Systems with Exogenous Lead Times . . . . . . . . . . . . . . . . 150

5.5.1 No Inventory Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1515.5.2 Inventory Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.6 Capacitated Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1555.6.1 No Inventory and Capacity Sharing . . . . . . . . . . . . . . . . . . . 1555.6.2 With Inventory and Capacity Sharing . . . . . . . . . . . . . . . . . 158

5.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1605.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

6 Production Systems Engineering: Review and RecentDevelopments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Jingshan Li, Semyon M. Meerkov, and Liang Zhang6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1676.2 Production Systems and Performance Metrics

Addressed in PSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686.2.1 Block Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686.2.2 Parameters of Machines and Buffers . . . . . . . . . . . . . . . . . . 1716.2.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

6.3 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1726.3.1 Performance Evaluation Using Aggregation Approach . . 1726.3.2 Performance Evaluation Using PSE Toolbox . . . . . . . . . . . 176

6.4 Fundamental Laws of PSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1776.4.1 First Uptime vs. Downtime Law . . . . . . . . . . . . . . . . . . . . . 1776.4.2 Second Uptime vs. Downtime Law . . . . . . . . . . . . . . . . . . . 1786.4.3 Reversibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

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6.4.4 Monotonicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1826.4.5 Improvability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

6.5 Bottleneck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1876.5.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1876.5.2 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1886.5.3 Buffering Potency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

6.6 Leanness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1916.6.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1916.6.2 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

6.7 Production Lead Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1946.7.1 Model and Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1956.7.2 Identical Machines Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1956.7.3 Non-identical Machines Case . . . . . . . . . . . . . . . . . . . . . . . 198

6.8 Re-entrant Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1996.8.1 Model, Equations, and Problems . . . . . . . . . . . . . . . . . . . . . 2006.8.2 Equilibria and Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2026.8.3 Transients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

6.9 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

7 Production Release Control: Paced, WIP-Basedor Demand-Driven? Revisiting the Push/Pull and Make-to-Order/Make-to-Stock Distinctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211George Liberopoulos7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2117.2 Production Control in the Absence of Demands . . . . . . . . . . . . . . . . 215

7.2.1 System Without WIP Control . . . . . . . . . . . . . . . . . . . . . . . 2157.2.2 Systems with WIP Control . . . . . . . . . . . . . . . . . . . . . . . . . . 217

7.3 Production Control in the Presence of Demands . . . . . . . . . . . . . . . . 2237.3.1 System Without WIP Control in the Presence

of Demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2237.3.2 Systems with WIP Control in the Presence of Demands . . 228

7.4 Production Control with Advance Demand Informationand Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2387.4.1 Systems with Advance Demand Information . . . . . . . . . . . 2387.4.2 Production Control Systems with Forecasts . . . . . . . . . . . . 241

7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

8 Queueing Network Models of Material Handlingand Transportation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249J. MacGregor Smith8.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

8.1.1 Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2498.1.2 Outline of Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Contents xix

8.2 Problem Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2508.2.1 Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2538.2.2 Conveyors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2538.2.3 Restricted Area units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2558.2.4 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2558.2.5 Material Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2558.2.6 Material Handling Systems . . . . . . . . . . . . . . . . . . . . . . . . . 256

8.3 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2578.3.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2588.3.2 State Dependent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2598.3.3 M/G/c/c Probability Distribution . . . . . . . . . . . . . . . . . . . 261

8.4 Product Form Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2648.4.1 Product Form Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2658.4.2 Open Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2658.4.3 Closed Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2718.4.4 Engset Loss Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728.4.5 Mixed Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274

8.5 Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2778.5.1 Optimal Topology Problems (OTOP) . . . . . . . . . . . . . . . . . 2788.5.2 Optimal Routing Problems (ORTE) . . . . . . . . . . . . . . . . . . 2798.5.3 Optimal Resource Allocation Problems (ORAP) . . . . . . . . 279

8.6 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

9 Modeling and Analysis of Output Variability in Discrete MaterialFlow Production Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Barıs Tan9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

9.1.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2889.2 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291

9.2.1 Number of Parts Produced . . . . . . . . . . . . . . . . . . . . . . . . . . 2919.2.2 Time to Produce a Given Order . . . . . . . . . . . . . . . . . . . . . . 2919.2.3 Probability of Completing an Order on Time . . . . . . . . . . . 2929.2.4 State-Space Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293

9.3 Asymptotic Variance Rate of Output: V . . . . . . . . . . . . . . . . . . . . . . . 2949.3.1 Asymptotic Variance Rate of Output from Production

Lines with No Interstation Buffers . . . . . . . . . . . . . . . . . . . 2949.3.2 Asymptotic Variance Rate of Output from Production

Systems with Finite Buffers . . . . . . . . . . . . . . . . . . . . . . . . . 2989.4 Variance of the Number of Products Produced in a Given Time

Period: Var[N(t)] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3029.4.1 Determining the Variance Rate of the Output from the

Probability Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3039.4.2 Variance Rate of the Output from a Two-Machine Line

with a Finite Buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

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10 Stochastic Lot Sizing Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Horst Tempelmeier10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31310.2 Stochastic Dynamic Single-Item Lot Sizing Models . . . . . . . . . . . . 318

10.2.1 Static Uncertainty Strategy: Fixed ReplenishmentPeriods, Fixed Lot Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320

10.2.2 Fixed Replenishment Periods, Variable Lot Sizes . . . . . . . 33210.3 Stochastic Dynamic Multi-item Capacitated Lot Sizing Models . . . 336

10.3.1 Solution Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33810.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

11 From Operational to Financial Evaluationof Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Nico J. Vandaele11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34511.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346

11.2.1 Operational Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34711.2.2 Financial Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34711.2.3 Market Effects of Shorter Lead Time . . . . . . . . . . . . . . . . . 349

11.3 Maximizing Profit Within an Integrated Queueing Model . . . . . . . . 34911.3.1 The Queueing Model Incorporating Lot Sizing . . . . . . . . . 35011.3.2 The Queueing Model Incorporating Overtime . . . . . . . . . . 35111.3.3 Operational Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35211.3.4 Objective Function in Terms of Profit . . . . . . . . . . . . . . . . . 35211.3.5 The Demand Side: Sales Price as a Function

of The Lead Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35611.3.6 The Complete Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

11.4 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35811.4.1 Managerial Decision Making Based on Economic

Value Added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35811.4.2 Analysis of Demand Sensitivity . . . . . . . . . . . . . . . . . . . . . . 36011.4.3 Analysis of a Multi-product, Multi-machine Example . . . 361

11.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369

9.5 Distribution of the Number of Products Produced in a GivenTime Period: P[N(t) = n] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

9.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

Contributors

Chapter 1: The Design of Manufacturing Systems to Cope with Variability

John A. Buzacott Schulich School of Business, York University, Toronto, ON,Canada

John Buzacott was born in Sydney, Australia. After graduating in Physics and inElectrical Engineering from the University of Sydney he worked and studied in theUK, obtaining Masters and Ph.D. degrees from the University of Birmingham. Hehas been a faculty member at the University of Toronto, the University of Waterlooand York University. He has been President of the Canadian Operational ResearchSociety and President of the Production and Operations Management Society. He isa Fellow of INFORMS and of POMS. In 2001 he was awarded the degree of DoctorHonoris Causa by the Technical University of Eindhoven in the Netherlands. He isnow retired and lives in Toronto.

Chapter 2: Modeling Automated Warehouses Using Semi-Open QueueingNetworks

Xiao Cai FedEx Corporation, Memphis, TN, USAXiao Cai is the Senior Project Marketing Analyst in FedEx Services. She re-

ceived her Ph.D. degree in Industrial Engineering Department from the Universityof Louisville. She is focusing on business oriented revenue strategy analysis andstatistical optimization models, conducting innovative customer segmentation anddata mining, analyzing complicated big data among multiple databases to recom-mend solid marketing strategies to senior managers, leading and cooperating withteam peers in complex projects and quickly absorbing new technologies.

Sunderesh S. Heragu University of Louisville, Louisville, KY, USASunderesh S. Heragu is Professor and the Mary Lee and George F. Duthie Chair

in Engineering Logistics in the Industrial Engineering department at the Univer-sity of Louisville. He is also Director of the Logistics and Distribution Institute(LoDI). Previously he was Professor of Decision Sciences and Engineering Sys-tems at Rensselaer Polytechnic Institute. He has taught at State University of NewYork, Plattsburgh and held visiting appointments at State University of New York,

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xxii Contributors

Buffalo, Technical University of Eindhoven and University of Twente, in the Nether-lands and IBM’s Thomas J. Watson Research Center in Yorktown Heights, NY.

Yang Liu Chrysler Group LLC, Belvidere, IL, USAYang Liu’s primary research interests are stochastic modeling, analysis and con-

tinuous improvement of manufacturing, logistics and healthcare systems. He cur-rently works as a quality engineer at Chrysler Group. He previously worked for Dr.Heragu as Postdoctoral Research Associate at the University of Louisville. He re-ceived a B.S. and M.S. in Automation from Tsinghua University in China, a Ph.D.in Electrical Engineering from the University of Kentucky.

Chapter 3: Exact Analysis of Discrete Part Production Lines: The MarkovianQueueing Network and the Stochastic Automata Networks Formalisms

P. Fernandes Department of Computer Science, PUCRS-PPGCC, Porto Alegre,Brazil

P. Fernandes has a B.Sc. and M.Sc. in Computer Science by Federal Universityof Rio Grande do Sul, Brazil (1987 and 1990), Ph.D. in Computer Science by INPGrenoble, France (1998). Professor in Performance Evaluation/Stochastic Modelingand Director of the Graduate Program on Computer Science of the Informatics De-partment from PUCRS University, Porto Alegre, Brazil. Prof. Fernandes also servesat the International Relations Office of PUCRS University. Prof. Fernandes has pub-lished over 80 journal and conference papers in many domains of Computer Science.Among other research activities, Prof. Fernandes has coordinated research programsbetween academia and industries, e.g., Petrobras and Siemens. He has been generaland program chair of several conferences, e.g., IEEE International Conference onGlobal Software Engineering and Brazilian Performance Evaluation Workshop. Un-der Prof. Fernandes personal supervision 23 M.Sc. and 2 Ph.D. students were gradu-ated at PUCRS Computer Science Graduated Program. His research interests covermany aspects of performance evaluation and stochastic modeling, but also numeri-cal methods, data mining algorithms, software engineering, and formal methods.

M.E.J. O’Kelly Waterford Institute of Technology, IrelandAfter competing a bachelor’s degree in Electrical Engineering at the National

University of Ireland (Cork), MEJ O’Kelly (Eddie) went on to study MechanicalEngineering at Caltech and Industrial and Management Engineering at ColumbiaUniversity, New York. He received a Ph.D in Applied Mechanics and Economicsfrom Caltech. Following a period in the electronics industry in France and Irelandhe was appointed Head of Manpower Forecasting in the Department of Labour,Dublin. Eddie O’Kelly set up the Department of Industrial Engineering at NationalUniversity of Ireland, Galway (NUI,G), where he was Chair of the Departmentfor over 30 years. He has been Deputy Chairman of the Electricity Supply Board,the national integrated electricity utility and Chairman of EirGrid, the national in-dependent transmission system operator. Currently, he is associated with the Eu-gene Lawlor Graduate School of Mathematics, Waterford Institute of Technology,Ireland.

Contributors xxiii

C.T. Papadopoulos Department of Economics, Aristotle University of Thessa-loniki, Thessaloniki, Greece

Professor in Quantitative Methods in Production/Operations Management, andDirector of the Graduate Program on “Management and Economics” of the De-partment of Economics, Aristotle University of Thessaloniki, Greece. Prof. Pa-padopoulos T. Chrissoleon has published over 30 journal papers, 2 books and 2edited books in the area of Stochastic Modeling of Manufacturing and Service Op-erations (SMMSO) and many Conference papers. He is member of the EditorialBoard of the International journals: International Journal of Production Research(IJPR), Computers and Industrial Engineering (CAIE), Decision Making in Manu-facturing and Services, and ex Department Editor of the IIE Transactions and otherjournals in Operations Management and Logistics/Supply Chain Management. Heis Guest co-Editor of the journal Annals of Operations Research (ANOR). Profes-sor Papadopoulos is referee/reviewer of over 20 international journals in the area ofSMMSO. He has been the founding Organizer/Chair of the Series of InternationalConferences in the area of SMMSO and has organized the first five Conferencesof this Series and the 30th CAIE international Conference. Prof. Papadopoulos hassupervised several Master and Ph.D. theses at various Universities (in Ireland andGreece) and has been teaching several courses and modules in Operations Manage-ment, Operations Research, Probability and Statistics and Design Management atboth under- and post-graduate level.

He has a Ph.D. in Industrial Engineering/Operations Research (Thesis Title:“Mathematical Modeling of Reliable Production Lines”), Department of Indus-trial Engineering, National University of Ireland, Galway (former UCG), Ireland,1989. M.Sc. in Operations Research and Computer Science (Thesis Title: “Stochas-tic Inventory Models”), National Kapodestrian University of Athens, Department ofMathematics, Greece, 1983. B.Sc. in Mathematics, Aristotle University of Thessa-loniki, Department of Mathematics, 1981.

A. Sales Department of Computer Science, PUCRS-PPGCC, BrazilAfonso Sales is university lecturer in Computer Science at Pontifical Catholic

University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil. He is also currentlya fellow researcher in Performance Evaluation Group (PEG) at PUCRS. Afonso gothis Ph.D. (2009) in Computer Science degree from Grenoble Institute of Technology(Grenoble INP), France. He has wide knowledge about state space generation tech-niques using decision diagrams and numerical solution methods based on structureddescription of Markovian models. Afonso also spent 3 years as Software Engineerat Hewlett-Packard Brazil R and D team. His research interests include stochasticmodeling and simulation, continuous and discrete time modeling, structured Marko-vian formalisms, such as Stochastic Automata Networks (SAN) and Stochastic PetriNets (SPN), structured and Kronecker based approaches for Markov analysis, modelchecking, as well as performance evaluation of systems applied to several domains,such as software engineering, performance testing, computer networks, and paralleland distributed computing.

xxiv Contributors

Chapter 4: Models of Leveling for Lean Manufacturing Systems

Kai Furmans IFL, Karlsruhe Institute of Technology, Karlsruhe, GermanyKai Furmans is full Professor and head of the Institute for Material Handling and

Logistics inside the Karlsruhe Institute of Technology, Germany. He graduated witha Diploma in Industrial Engineering (Wirtschaftsingenieur) from Karlsruhe Univer-sity in 1988, received his Ph.D. in Mechanical Engineering also from KarlsruheUniversity in 1992. As post-doc he was a visiting researcher at Thomas J. Wat-son Labs IBM in Yorktown Heights, and received his Venda legends for Logisticsin 2000 from Karlsruhe University. He worked from 1996–2003 for Robert BoschGmbH in several positions in Logistics, his final position being head of divisionallogistics for the Thermotechnology division. His research interests are design ofmaterial handling systems and models for Supply Chains.

Martin Veit Robert Bosch GmbH, Gerlingen, GermanyDr.-Ing. Martin Veit is a manager in the corporate logistics department of Robert

Bosch GmbH. Currently his work is focused on projects introducing lean methodsin warehousing. Before that he worked as a consultant in McKinsey’s manufactur-ing practice. His initial interest in lean started as a research assistant at KarlsruheInstitute of Technology (KIT), where he received a Ph.D. for his thesis on modelsfor buffer sizing in Heijunka leveled supply chains.

Chapter 5: Value of Advance Demand Information in Production and Inven-tory Systems with Shared Resources

Fikri Karaesmen Department of Industrial Engineering, Koc University, Sarıyer,Istanbul, Turkey

Fikri Karaesmen is currently Professor of Industrial Engineering at Koc Univer-sity, Istanbul, Turkey. He received his B.S. degree from METU (Ankara, Turkey)in 1990 and his Ph.D. from Northeastern University (Boston, USA) in 1996. Hisresearch mainly focuses on stochastic models of production/inventory systems andservice operations. His papers have appeared in Management Science, OperationsResearch, Manufacturing and Service Operations Management, European Journalof Operational Research and other journals. He is currently on the editorial boardsof Management Science, Manufacturing and Service Operations Management, IIETransactions and 4OR.

Chapter 6: Production Systems Engineering: Review and Recent Develop-ments

Jingshan Li Department of Industrial and Systems Engineering, University ofWisconsin-Madison, WI, USA

Jingshan Li received the B.S., M.S., and Ph.D. degrees from Tsinghua Univer-sity, Chinese Academy of Sciences, and University of Michigan in 1989, 1992, and2000, respectively. He was with General Motors Research and Development Centerfrom 2000 to 2006, and with University of Kentucky from 2006 to 2010. He is nowan Associate Professor in Department of Industrial and Systems Engineering, Uni-versity of Wisconsin, Madison, WI. He received the NSF Career Award, Best Paper

Contributors xxv

Awards from IEEE Transactions on Automation Science and Engineering and IIETransactions. He is a Department Editor of IIE Transactions, and Associate Editor ofIEEE Transactions on Automation Science and Engineering and International Jour-nal of Production Research. His primary research interests are in modeling, analysisand improvement of manufacturing and health care systems.

Semyon M. Meerkov Department of Electrical Engineering and Computer Science,University of Michigan, Ann Arbor, MI, USA

Semyon M. Meerkov received his MSEE degree from the Polytechnic of Kharkov,Kharkov, Ukraine, in 1962 and Ph.D. in Systems Science from the Institute of Con-trol Sciences, Moscow, Russia, in 1966. He was with the Institute of Control Sci-ences until 1977. From 1979 to 1984 he was with the Department of Electrical andComputer Engineering, Illinois Institute of Technology, Chicago, IL. Since 1984 hehas been a Professor at the Department of Electrical Engineering and Computer Sci-ence of the University of Michigan, Ann Arbor, MI. He has held visiting positionsat UCLA (1978–1979), Stanford University (1991), Technion, Israel (1997–1998and 2008), Tsinghua University, Beijing, China (2008), and Ben-Gurion Universityof the Negev, Beer-Sheva, Israel (2011). He was the Editor-in-Chief of Mathemati-cal Problems in Engineering, Department Editor for Manufacturing Systems of IIETransactions and Associate Editor of several other journals. His research interestsare in Systems and Control with applications to production systems and communi-cation networks and in Mathematical Theory of Rational Behavior with applicationsresilient monitoring and control.

Liang Zhang Department of Industrial and Manufacturing Engineering, Universityof Wisconsin-Milwaukee, WI, USA

Liang Zhang received his B.E. and M.E. degrees from Center for Intelligentand Networked Systems (CFINS), Department of Automation, Tsinghua University,Beijing, China, in 2002 and 2004, respectively, and his Ph.D. degree in ElectricalEngineering—Systems from the University of Michigan, Ann Arbor, USA, in 2009.He is currently an Assistant Professor at the Department of Industrial and Manufac-turing Engineering, University of Wisconsin-Milwaukee, USA. His research inter-ests include modeling, analysis, improvement, design, control, and energy-efficientoperations of manufacturing and service systems, and mathematical analysis andfeedback control of battery management systems.

Chapter 7: Production Release Control: Paced, WIP-Based or Demand-Driven?Revisiting the Push/Pull and Make-to-Order/Make-to-Stock Distinctions

George Liberopoulos Department of Mechanical Engineering, University of Thes-saly, Volos, Greece

George Liberopoulos (BS’85, M.Eng.’86, Mech. Eng., Cornell University;PhD’93, Manufacturing Eng., Boston University) is Professor of Production Man-agement and Director of the Production Management Laboratory in the Departmentof Mechanical Engineering (DME) at the University of Thessaly (UTh), Greece.Prior to joining UTh, he was Lecturer in the Department of Manufacturing Engi-neering at Boston University (1993) and Visiting Research Scientist in Laboratoire

xxvi Contributors

d’Informatique de Paris 6 (LIP6) at Universit Pierre et Marie Curie (UPMC)/CNRS,France (1994–1996). He has served as Vice Chairman (2005–2007), Director ofPostgraduate Studies (2005–2007) and Chairman (2007–2009) in the DME, andVice President of the University Research Committee at UTH (2005–2008). Since2011, he is a board member of Greece’s Regulatory Authority for Railways. Heis/was a member of the editorial boards of FSMJ, IIE-T and OR Spectrum. He hasco-edited a book (Springer) and seven special issues of ANOR, IIE-T and OR Spec-trum, with topics in the area of stochastic modeling of manufacturing and servicesystems. He has published over 50 articles in conference proceedings, books andscientific journals, including OR, JOTA, EJOR, ANOR, OR Spectrum, Interfaces,IEEE-TAC, IIE-T, MSOM, IJPE, IJPR, and has participated in over 20 grants. Hehas supervised four Ph.D. dissertations, over 30 M.Sc. theses, and over 40 Diplomatheses. His research interests focus on systems analysis with the use of operationsresearch, applied probability and automatic control methods, and applications inproduction/operations planning/control, supply chain management, and design ofelectricity markets, among others.

Chapter 8: Queueing Network Models of Material Handling and Transporta-tion Systems

J. MacGregor Smith Department of Mechanical and Industrial Engineering,University of Massachusetts-Amherst, MA, USA

Professor Smith graduated with a B.Arch and M.Arch from the University ofCalifornia at Berkeley and a Ph.D. in Operations Research from the Universityof Illinois in Champaign-Urbana. Professor Smith conducts research on topologi-cal network design, stochastic network design and analysis, and facility layout andlocation problems. In particular, he is doing research on Steiner minimal trees in3d, applications of Steiner Trees to Minimum Energy Configurations (MEC’s) andprotein modelling. He is also working on state dependent queueing network analy-sis and finite buffer queueing network models, quadratic assignment and set pack-ing problems. Applications include the design and layout of manufacturing plants,health care facilities, and many other production and service oriented systems. Oneof the unique modelling tools developed in our research is concerned with dynamictraffic flow models using queueing theory and queueing networks. Recently, Pro-fessor Smith spent a sabbatical in Greece after receiving a Fulbright Fellowship tospend a semester at the University of Piraeus in Piraeus, Greece.

Chapter 9: Modeling and Analysis of Output Variability in Discrete MaterialFlow Production Systems

Barıs Tan College of Administrative Sciences and Economics, Koc University,Sarıyer, Istanbul, Turkey

Barıs Tan is a professor of operations management at Koc University, Istanbul,Turkey. He received a BS degree in Electrical and Electronics Engineering fromBogazici University (Turkey), an ME degree in Industrial and Systems Engineer-ing, an MSE in Manufacturing Systems Management, and a Ph.D. degree in Opera-tions Research from the University of Florida (USA). His research areas are design

Contributors xxvii

and control of production systems, modelling of manufacturing systems, stochas-tic modelling, and supply chain management. He held visiting positions at Har-vard University Center for Textile and Apparel Research, Massachusetts Instituteof Technology Operations Research Center, and MIT Laboratory for Manufacturingand Productivity. He is the recipient of TUBITAK fellowship, Turkish Academy ofSciences Distinguished Young Scholar Award, and Nato Science Fellowship. Pro-fessor Tan serves on the Boards of European Foundation for Management Devel-opment, CEMS Global Alliance in Management Education, and TUSIAD-KU Eco-nomic Research Forum. He is a member of Turkish Operations Research Society,Sloan Foundation Industry Studies program, INFORMS and Lean Institute Turkey.

Chapter 10: Stochastic Lot Sizing Problems

Horst Tempelmeier Department of Supply Chain Management and Production,University of Cologne, Cologne, Germany

Horst Tempelmeier is Full Professor of Supply Chain Management and Produc-tion at the University of Cologne, Germany. He obtained his Ph.D. in 1979 fromthe University of Trier, where he obtained his Habilitation in 1982. Prior to joiningthe University of Cologne faculty, he has been Full Professor of Production Man-agement at the Technical University of Darmstadt (1985–1989) and Braunschweig(1989–1993). He is co-founder of a german consulting company and has worked ona consulting basis with many major german companies.

His research interests include supply chain management and lot sizing as well asthe design of production systems, such as flexible manufacturing systems and flex-ible flow production systems. He has published numerous papers in such journalsas European Journal of Operational Research, OR Spectrum, International Journalof Production Research, Management Science, and others. He has (co-) authoredseveral standard text books (in german and english) on production and logistics,inventory management and flexible manufacturing systems.

Chapter 11:From Operational to Financial Evaluation of ManufacturingSystems

Nico J. Vandaele Faculty of Business and Economics, Katholieke Universiteit Leu-ven, Leuven, Belgium

Nico Vandaele holds a degree in Commercial Engineering (1990) and obtaineda Ph.D. in Applied Economics, Operations Research and Operations Managementfrom the K.U. Leuven in 1996. He is currently Full Professor Operations Manage-ment at the Katholieke Universiteit Leuven, Faculty of Business and Economics.He is a research member of the Research Center of Operations Management. Heis also a visiting researcher at CORE and IAG (Universit Catholique de Louvain).Nico Vandaele teaches courses in operations research and operations management.

His research interests are situated in modeling of manufacturing and service sys-tems, performance measurement, the design of planning systems, factory physics,health care management and traffic modeling. Recently new research has been setup in the area of decision support for product design and development. He publishedin leading journals like IIE Transactions, Managements Science, Transportation

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Research, European Journal of Operational Research, Interfaces, MSOM journal,Robotics and Intelligent Systems, International Journal of Production Economics,Computers and Operations Research, among others. He is active in several execu-tive training programs, both national and international, and has served as consul-tant/advisor for major global companies, like Abinbev, Atlas Copco, IBM, Baxter,Johnson & Johnson, Continental, Glaxo-Smith Kline, Monsanto, Bekaert, Procter& Gamble, as well as small and medium sized companies.