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Fierce competition in today's global market provides a powerful motivation for developing ever more sophisticated logistics systems. This book, written for the logistics manager and researcher, presents a survey of the modern theory and application of logistics. The goal of the book is to present the state of the art in the science of logistics management. As a result, the authors have written a timely and authoritative survey of this field that many practitioners and researchers will find makes an invaluable companion to their work.

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CONTENTS

Preface = ⅴ

1 Introduction = 1

 1.1 What Is Logistics Management = 1

 1.2 Examples = 3

 1.3 Modeling Logistics Problems = 6

 1.4 Logistics in Practice = 7

 1.5 Evaluation of Solution Techniques = 8

 1.6 Additional Topics = 9

 1.7 Book Overview = 10

Ⅰ Performance Analysis Techniques = 13

 2 Worst-Case Analysis = 15

  2.1 Introduction = 15

  2.2 The Bin-Packing Problem = 16

   2.2.1 First-Fit and Best-Fit = 18

   2.2.2 First-Fit Decreasing and Best-Fit Decreasing = 21

  2.3 The Traveling Salesman Problem = 22

   2.3.1 A Minimum Spanning Tree Based Heuristic = 23

   2.3.2 The Nearest Insertion Heuristic = 24

   2.3.3 Christofides' Heuristic = 28

   2.3.4 Local Search Heuristic = 29

  2.4 Exercises = 32

 3 Average-Case Analysis = 37

  3.1 Introduction = 37

  3.2 The Bin-Packing Problem = 38

  3.3 The Traveling Salesman Problem = 43

  3.4 Exercises = 48

 4 Mathematical Programming Based Bounds = 51

  4.1 Introduction = 51

  4.2 An Asymptotically Tight Linear Program = 52

  4.3 Lagrangian Relaxation = 55

  4.4 Lagrangian Relaxation and the Traveling Salesman Problem = 57

   4.4.1 The 1-Tree Lower Bound = 57

   4.4.2 The 1-Tree Lower Bound and Lagrangian Relaxation = 59

  4.5 The Worst-Case Effectiveness of the 1-tree Lower Bound = 60

  4.6 Exercises = 64

Ⅱ Vehicle Routing Models = 67

 5 The Capacitated VRP with Equal Demands = 69

  5.1 Introduction = 69

  5.2 Worst-Case Analysis of Heuristics = 70

  5.3 The Asymptotic Optimal Solution Value = 75

  5.4 Asymptotically Optimal Heuristics = 76

  5.5 Exercises = 80

 6 The Capacitated VRP with Unequal Demands = 81

  6.1 Introduction = 81

  6.2 Heuristics for the CVRP = 81.

  6.3 Worst-Case Analysis of Heuristics = 85

  6.4 The Asymptotic Optimal Solution Value = 88

   6.4.1 A Lower Bound = 89

   6.4.2 An Upper Bound = 92

  6.5 Probabilistic Analysis of Classical Heuristics = 94

   6.5.1 A Lower Bound = 96

   6.5.2 The UOP(α) Heuristic = 97

  6.6 The Uniform Mode = l99

  6.7 The Location-Based Heuristic = 102

  6.8 Rate of Convergence to the Asymptotic Value = 105

  6.9 Exercises = 105

 7 The VRP with Time Window Constraints = 107

  7.1 Introduction = 107

  7.2 The Model = 107

  7.3 The Asymptotic Optimal Solution Value = 109

  7.4 An Asymptotically Optimal Heuristic = 114

   7.4.1 The Location-Based Heuristic = 115

   7.4.2 A Solution Method for CVLPTW = 117

   7.4.3 Implementation = 118

   7.4.4 Numerical Study = 119

  7.5 Exercises = 122

 8 Solving the VRP Using a Column Generation Approach = 125

  8.1 Introduction = 125

  8.2 Solving a Relaxation of the Set-Partitioning Formulation = 126

  8.3 Solving the Set-Partitioning Problem = 130

   8.3.1 Identifying Violated Clique Constraints = 132

   8.3.2 Identifying Violated Odd Hole Constraints = 132

  8.4 The Effectiveness of the Set-Partitioning Formulation = 133

   8.4.1 Motivation = 133

   8.4.2 Proof of Theorem 8.4.1 = 135

  8.5 Exercises = 138

Ⅲ Inventory Models = 143

 9 Economic Lot Size Models with Constant Demands = 145

  9.1 Introduction = 145

   9.1.1 The Economic Lot Size Model = 145

   9.1.2 The Finite Horizon Model = 147

   9.1.3 Power of Two Policies = 149

  9.2 Multi-Item Inventory Models = 151

   9.2.1 Introduction = 151

   9.2.2 Notation and Assumptions = 153

   9.2.3 Worst-Case Analyses = 153

  9.3 A Single Warehouse Multi-Retailer Model = 158

   9.3.1 Introduction = 158

   9.3.2 Notation and Assumptions = 158

  9.4 Exercises = 163

 10 Economic Lot Size Models with Varying Demands = 165

  10.1 The Wagner-Whitin Model = 165

  10.2 Models with Capacity Constraints = 171

  10.3 Multi-Item Inventory Models = 175

  10.4 Exercises = 177

 11 Stochastic Inventory Models = 179

  11.1 Introduction = 179

  11.2 Single Period Models = 180

  11.3 Finite Horizon Models = 181

  11.4 Quasiconvex Loss Functions = 188

  11.5 Infinite Horizon Models = 192

  11.6 Multi-Echelon Systems = 195

  11.7 Exercises = 197

Ⅳ Hierarchical Models = 201

 12 Facility Location Models = 203

  12.1 Introduction = 203

  12.2 An Algorithm for the p-Median Problem = 204

  12.3 An Algorithm for the Single-Source Capacitated Facility Location Problem = 208

  12.4 A Distribution System Design Problem = 211

  12.5 The Structure of the Asymptotic Optimal Solution = 215

  12.6 Exercises = 216

 13 Integrated Logistics Models = 219

  13.1 Introduction = 219

  13.2 Single Warehouse Models = 221

  13.3 Worst-Case Analysis of Direct Shipping Strategies = 222

   13.3.1 A Lower Bound = 223

   13.3.2 The Effectiveness of Direct Shipping = 224

  13.4 Asymptotic Analysis of ZIO Policies = 225

   13.4.1 A Lower Bound on the Cost of Any Policy = 227

   13.4.2 An Efficient Fixed Partition Policy = 228

  13.5 Asymptotic Analysis of Cross-Docking Strategies = 232

  13.6 An Algorithm for Multi-Echelon Distribution Systems = 234

  13.7 Exercises = 235

Ⅴ Logistics Algorithms in Practice = 237

 14 A Case Study : School Bus Routing = 239

  14.1 Introduction = 239

  14.2 The Setting = 240

  14.3 Literature Review = 242

  14.4 The Problem in New York City = 243

  14.5 Distance and Time Estimation = 245

  14.6 The Routing Algorithm = 247

  14.7 Additional Constraints and Features = 251

  14.8 The Interactive Mode = 253

  14.9 Data, Implementation and Results = 254

 15 A Decision Support System for Network Configuration = 255

  15.1 introduction = 255

  15.2 Data Collection = 257

  15.3 The Baseline Feature = 262

  15.4 Flexibility and Robustness = 263

  15.5 Exercises = 264

References = 265

Index = 277