Computational Intelligence in Logistics and Supply Chain Management (eBook)

Thomas Hanne received master's degrees in Economics and Computer Science, and a PhD in Economics. From 1999 to 2007 he worked at the Fraunhofer Institute for Industrial Mathematics (ITWM) as senior scientist. Since then he is Professor for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland and Head of Competence Center Systems Engineering since 2012. Thomas Hanne is author of about 80 journal and conference articles and editor of several journals and special issues. His current research interests include multicriteria decision analysis, evolutionary algorithms, metaheuristics, optimization, simulation, logistics, and supply chain management. Rolf Dornberger is the head of the Institute for Information Systems, School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW (since 2007) and the head of the competence centers New Trends & Innovation (since 2013) and Technology, Organization & People (since 2014) and was head of the competence center Systems Engineering (2006 - 2010). In 2002, he was appointed associate professor and, in 2003, full professor for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland FHNW or rather at its predecessor the University of Applied Sciences Solothurn Switzerland. Additionally, he was a part-time lecturer and visiting professor at the University of Stuttgart and the University of Applied Sciences Zurich. Before returning to academy, he worked in industry in different management positions as a consultant, IT officer and senior researcher in different engineering, technology and IT companies in the field of power generation systems and IT solutions for the airline business. He holds a PhD (1998) and a Diploma degree in Aerospace Engineering (1994). His current research interests include computational intelligence, optimization, innovation and technology management, and new trends and innovations.

Preface 6
Acknowledgments 8
Contents 10
List of Symbols 14
List of Abbreviations and Acronyms 18
Chapter 1: Introduction to Logistics and Supply Chain Management 22
1.1 The Concept of Logistics and Supply Chain Management 22
1.2 A Short History of Logistics 25
1.3 Recent Trends and the Modern Importance of Logistics 26
1.4 The Need for a Better Planning 30
References 33
Chapter 2: Computational Intelligence 34
2.1 Foundations of Computational Intelligence 34
2.1.1 Artificial and Computational Intelligence and Related Techniques 35
2.1.1.1 Artificial Intelligence 35
2.1.1.2 Computational Intelligence 36
2.1.1.3 Techniques Related to Artificial and Computational Intelligence 36
2.1.1.4 Interest in Computational Intelligence 37
2.1.2 Properties of Computational Intelligence 37
2.1.3 The Big Picture of Computational Intelligence 39
2.1.4 Application Areas of Computational Intelligence 41
2.1.4.1 Optimization and Search 41
2.1.4.2 Multiobjective Optimization 43
2.2 Methods of Computational Intelligence 43
2.2.1 Evolutionary Computation 43
2.2.2 Evolutionary Algorithms 44
2.2.2.1 Evolution Strategy 47
2.2.2.2 Genetic Algorithm 48
2.2.2.3 Genetic Programming 50
2.2.2.4 Evolutionary Programming 51
2.2.2.5 Multiobjective Evolutionary Algorithms 51
2.2.2.6 Memetic Algorithms 51
2.2.2.7 Other Evolutionary Computation Techniques 52
2.2.3 Swarm Intelligence 53
2.2.3.1 Particle Swarm Optimization 54
2.2.3.2 Discrete Particle Swarm Optimization 55
2.2.3.3 Ant Colony Optimization 55
2.2.4 Neural Networks 56
2.2.5 Fuzzy Logic 57
2.2.6 Artificial Immune System 57
2.2.7 Further Related Methods 57
2.2.7.1 Reinforcement Learning 57
2.2.7.2 Simulated Annealing 58
2.2.7.3 Further Metaheuristics: Local and Tabu Search 58
Local Search, Neighborhood Search 58
Tabu Search 59
Variable Neighborhood Search 59
Greedy Randomized Adaptive Search Procedure (GRASP) 60
References 60
Chapter 3: Transportation Problems 63
3.1 Assignment Problems 64
3.2 Shortest Paths 65
3.3 The Travelling Salesman Problem 67
3.4 Methods for Solving the Travelling Salesman Problem 70
3.4.1 Heuristics for the Travelling Salesman Problem 70
3.4.2 Evolutionary Algorithms for the Travelling Salesman Problem 71
3.4.3 Other Metaheuristics and Neural Networks for the Travelling Salesman Problem 75
3.4.4 On the Performance of Solution Approaches 76
3.5 The Vehicle Routing Problem 77
3.5.1 The Vehicle Routing Problem with Time Windows 79
3.5.2 The Vehicle Routing Problem with Multiple Vehicles 79
3.5.3 The Vehicle Routing Problem with Multiple Depots 80
3.5.4 More Differentiated Problem Variants 81
3.6 Solution Approaches for Vehicle Routing Problems 82
3.7 The Pickup and Delivery Problem 85
3.8 Network Flow Problems 87
References 88
Chapter 4: Inventory Planning and Lot-Sizing 92
4.1 The Need for Inventory Planning 92
4.2 Economic Order Quantities and Safety Stocks 94
4.3 Capacitated Lot-Sizing Problems 98
4.4 Solution Approaches for Capacitated Lot-Sizing Problems 102
4.5 Planning Warehouse Operations 104
4.6 Storage Locations 106
4.7 Inventory Routing 107
References 113
Chapter 5: Scheduling 117
5.1 Introduction 117
5.2 Simple Rules and Heuristics 118
5.3 Standard Scheduling Problems 121
5.3.1 Job Shop Scheduling 123
5.3.2 Flow Shop Scheduling 124
5.3.3 Open Shop Scheduling 125
5.4 Specific Scheduling Problems in Logistics 126
5.5 Solving Scheduling Problems with Computational Intelligence Techniques 128
5.5.1 Encoding Issues 128
5.5.2 Usage of Metaheuristics in Scheduling 133
References 135
Chapter 6: Location Planning and Network Design 138
6.1 Location Planning as Multicriteria Decision Problems 138
6.2 Discrete Location Problems 140
6.2.1 The p-Median Problem 141
6.2.1.1 Problem Statement 141
6.2.1.2 Solution Approaches 143
6.2.2 The p-Center Problem 145
6.2.2.1 Problem Statement 145
6.2.2.2 Solution Approaches 146
6.2.3 The Uncapacitated Facility Location Problem (UFLP) 147
6.2.3.1 Problem Statement 148
6.2.3.2 Solution Approaches 149
6.2.4 The Capacitated Facility Location Problem (CFLP) 150
6.2.4.1 Problem Statement 150
6.2.4.2 Solution Approaches 150
6.3 Continuous Location Problems 152
6.3.1 The Uncapacitated Multi-facility Weber Problem (UMWP) 152
6.3.1.1 Problem Statement 152
6.3.1.2 Solution Approaches 153
6.3.2 The Capacitated Multi-facility Weber Problem (CMWP) 155
6.3.2.1 Problem Statement 155
6.3.2.2 Solution Approaches 157
6.4 Location Routing Problems 158
6.5 Hub Location Problems 161
6.6 Multi-Echelon Network Design 162
6.7 Conclusions 163
References 163
Chapter 7: Intelligent Software for Logistics 169
7.1 General-Purpose Optimization Software 169
7.1.1 Setting Up a Suitable Model for the Optimization Software 171
7.1.2 Integration of Optimization Software with Logistics Applications 172
7.1.3 Adapting the Method to the Problem Under Consideration 173
7.2 Software Providing Specific Optimization Algorithms or Supporting Particular Optimization Problems 173
7.3 General-Purpose Business Software 176
7.4 Logistics Software 179
7.4.1 Warehouse Management Systems 179
7.4.2 Software for Transportation Planning 181
7.4.3 Packing and Loading Software 182
7.5 Conclusions 183
References 184
Authors Brief Biographies 186
Index 187