The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management

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Springer Science & Business Media, Jan 1, 1997 - Business & Economics - 281 pages
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Written for the logistics manager and researcher, this book presents a survey of the modern theory and application of logistics. The book includes two extensive case studies of the application of these ideas. Many practitioners and researchers will find this a timely and authoritative survey of the field and an invaluable companion to their work.
  

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Contents

Introduction
1
12 Examples
3
13 Modeling Logistics Problems
6
14 Logistics in Practice
7
15 Evaluation of Solution Techniques
8
16 Additional Topics
9
17 Book Overview
10
PERFORMANCE ANALYSIS TECHNIQUES
13
84 The Effectiveness of the SetPartitioning Formulation
133
842 Proof of Theorem
135
85 Exercises
138
INVENTORY MODELS
143
Economic Lot Size Models with Constant Demands
145
912 The Finite Horizon Model
147
913 Power of Two Policies
149
92 MultiItem Inventory Models
151

WorstCase Analysis
15
22 The BinPacking Problem
16
221 FirstFit and BestFit
18
222 FirstFit Decreasing and BestFit Decreasing
21
23 The Traveling Salesman Problem
22
231 A Minimum Spanning Tree Based Heuristic
23
232 The Nearest Insertion Heuristic
24
233 Christofides Heuristic
28
234 Local Search Heuristics
29
24 Exercises
32
AverageCase Analysis
37
32 The BinPacking Problem
38
33 The Traveling Salesman Problem
43
34 Exercises
48
Mathematical Programming Based Bounds
51
42 An Asymptotically Tight Linear Program
52
43 Lagrangian Relaxation
55
44 Lagrangian Relaxation and the Traveling Salesman Problem
57
442 The ITree Lower Bound and Lagrangian Relaxation
59
45 The WorstCase Effectiveness of the 1tree Lower Bound
60
46 Exercises
64
VEHICLE ROUTING MODELS
67
The Capacitated VRP with Equal Demands
69
52 WorstCase Analysis of Heuristics
70
53 The Asymptotic Optimal Solution Value
75
54 Asymptotically Optimal Heuristics
76
55 Exercises
80
The Capacitated VRP with Unequal Demands
81
63 WorstCase Analysis of Heuristics
85
64 The Asymptotic Optimal Solution Value
88
641 A Lower Bound
89
642 An Upper Bound
92
65 Probabilistic Analysis of Classical Heuristics
94
651 A Lower Bound
96
652 The UOPa Heuristic
97
66 The Uniform Model
99
67 The LocationBased Heuristic
102
68 Rate of Convergence to the Asymptotic Value
105
The VRP with Time Window Constraints
107
73 The Asymptotic Optimal Solution Value
109
74 An Asymptotically Optimal Heuristic
114
741 The LocationBased Heuristic
115
742 A Solution Method for CVLPTW
116
743 Implementation
118
744 Numerical Study
119
75 Exercises
122
Solving the VRP Using a Column Generation Approach
125
82 Solving a Relaxation of the SetPartitioning Formulation
126
83 Solving the SetPartitioning Problem
130
831 Identifying Violated Clique Constraints
132
922 Notation and Assumptions
153
93 A Single Warehouse MultiRetailer Model
158
94 Exercises
163
Economic Lot Size Models with Varying Demands
165
102 Models with Capacity Constraints
171
103 MultiItem Inventory Models
175
104 Exercises
177
Stochastic Inventory Models
179
112 Single Period Models
180
113 Finite Horizon Models
181
114 Quasiconvex Loss Functions
188
115 Infinite Horizon Models
192
116 MultiEchelon Systems
195
117 Exercises
197
HIERARCHICAL MODELS
201
Facility Location Models
203
122 An Algorithm for the p Median Problem
204
123 An Algorithm for the SingleSource Capacitated Facility Location Problem
208
124 A Distribution System Design Problem
211
125 The Structure of the Asymptotic Optimal Solution
215
126 Exercises
216
Integrated Logistics Models
219
132 Single Warehouse Models
221
133 WorstCase Analysis of Direct Shipping Strategies
222
1331 A Lower Bound
223
1332 The Effectiveness of Direct Shipping
224
134 Asymptotic Analysis of ZIO Policies
225
1341 A Lower Bound on the Cost of Any Policy
227
1342 An Efficient Fixed Partition Policy
228
135 Asymptotic Analysis of CrossDocking Strategies
232
136 An Algorithm for MultiEchelon Distribution Systems
234
137 Exercises
235
LOGISTICS ALGORITHMS IN PRACTICE
237
A Case Study School Bus Routing
239
142 The Setting
240
143 Literature Review
242
144 The Problem in New York City
243
145 Distance and Time Estimation
245
146 The Routing Algorithm
247
147 Additional Constraints and Features
251
148 The Interactive Mode
253
149 Data Implementation and Results
254
A Decision Support System for Network Configuration
255
152 Data Collection
257
153 The Baseline Feature
262
154 Flexibility and Robustness
263
155 Exercises
264
References
265
Index
277
Copyright

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Page 267 - Vehicle Routing. In The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, EL Lawler, JK Lenstra, AHG Rinnooy Kan, and D. B. Shmoys, (eds), 43l-448.

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About the author (1997)

Bramel is a former faculty at Columbia University and is now working on risk management for a large financial institution.

David Simchi-Levi is Professor of Engineering Systems at MIT and is considered one of the premier thought leaders in supply chain management. He coauthored the books "Managing the Supply Chain", "The Logic of Logistics" as well as the award-winning" Designing and Managing the Supply Chain". He is the founder of LogicTools (now part of IBM), which provides software solutions and professional services for supply chain planning

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