The logic of logistics: theory, algorithms, and applications for logistics and supply chain management
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| Previous Title: | Bramel, Julien The logic of logistics |
|---|---|
| Main Authors: | , , |
| Format: | Book |
| Language: | English |
| Published: |
New York
Springer
2005
|
| Edition: | 2. ed. |
| Series: | Springer series in operations research
|
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012958554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | XX, 355 S. graph. Darst. |
| ISBN: | 0387221999 |
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| 245 | 1 | 0 | |a The logic of logistics |b theory, algorithms, and applications for logistics and supply chain management |c David Simchi-Levi ; Xin Chen ; Julien Bramel |
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| adam_text | Contents
Preface v
1 Introduction 1
1.1 What Is Logistics Management? 1
1.2 Managing Cost and Uncertainty 3
1.3 Examples 4
1.4 Modeling Logistics Problems 7
1.5 Logistics in Practice 7
1.6 Evaluation of Solution Techniques 9
1.7 Additional Topics 10
1.8 Book Overview 11
1 Performance Analysis Techniques 12
2 Convexity and Super-modularity 13
2.1 Introduction 13
2.2 Convex Analysis 13
2.2.1 Convex Sets and Convex Functions 13
2.2.2 Continuity and Differentiability Properties 16
2.2.3 Characterization of Convex Functions 20
2.2.4 Convexity and Optimization 23
2.3 Supermodularity 24
2.4 Exercises 31
3 Worst-Case Analysis 33
3.1 Introduction 33
3.2 The Bin-Packing Problem 34
3.2.1 First-Fit and Best-Fit 36
xi
xii Contents
3.2.2 First-Fit Decreasing and Best-Fit Decreasing 39
3.3 The Traveling Salesman Problem 40
3.3.1 A Minimum Spanning Tree Based Heuristic 41
3.3.2 The Nearest Insertion Heuristic 42
3.3.3 Christofides Heuristic 46
3.3.4 Local Search Heuristics 49
3.4 Exercises 50
4 Average-Case Analysis 55
4.1 Introduction 55
4.2 The Bin-Packing Problem 56
4.3 The Traveling Salesman Problem 61
4.4 Exercises 66
5 Mathematical Programming Based Bounds 69
5.1 Introduction 69
5.2 An Asymptotically Tight Linear Program 70
5.3 Lagrangian Relaxation 73
5.4 Lagrangian Relaxation and the Traveling Salesman Problem .... 75
5.4.1 The 1-Tree Lower Bound 76
5.4.2 The 1-Tree Lower Bound and Lagrangian Relaxation .... 77
5.5 The Worst-Case Effectiveness of the 1-tree Lower Bound 78
5.6 Exercises 82
II Inventory Models 84
6 Economic Lot Size Models with Constant Demands 85
6.1 Introduction 85
6.1.1 The Economic Lot Size Model 85
6.1.2 The Finite Horizon Model 87
6.1.3 Power of Two Policies 89
6.2 Multi-Item Inventory Models 91
6.2.1 Introduction 91
6.2.2 Notation and Assumptions 93
6.2.3 Worst-Case Analyses 93
6.3 A Single Warehouse Multi-Retailer Model 98
6.3.1 Introduction 98
6.3.2 Notation and Assumptions 98
6.4 Exercises 103
7 Economic Lot Size Models with Varying Demands 105
7.1 The Wagner-Whitin Model 105
7.2 Models with Capacity Constraints Ill
7.3 Multi-Item Inventory Models 114
Contents xiii
7.4 Exercises 116
8 Stochastic Inventory Models 119
8.1 Introduction 119
8.2 Single Period Models 120
8.2.1 The Model 120
8.3 Finite Horizon Models 12i
8.3.1 Model Description 121
8.3.2 ^-Convex Functions 123
8.3.3 Main Results 126
8.4 Quasiconvex Loss Functions 127
8.5 Infinite Horizon Models 130
8.6 Multi-Echelon Systems 137
8.7 Exercises 139
9 Integration of Inventory and Pricing 141
9.1 Introduction 141
9.2 Single Period Models 142
9.3 Finite Horizon Models 145
9.3.1 Model Description 145
9.3.2 Symmetric K-Convex Functions 148
9.3.3 Additive Demand Functions 153
9.3.4 General Demand Functions 155
9.3.5 Special Case: Zero Fixed Ordering Cost 156
9.3.6 Extensions and Challenges 157
9.4 Risk Averse Inventory Models 158
9.4.1 Expected utility risk averse models 159
9.4.2 Exponential utility risk averse models 161
9.5 Exercises 163
III Design and Coordination Models 166
10 Procurement Contracts 167
10.1 Introduction 167
10.2 Wholesale Contracts 169
10.3 Buy Back Contracts 171
10.4 Revenue Sharing Contracts 172
10.5 Portfolio Contracts 173
10.6 Exercises 177
11 Supply Chain Planning Models 179
11.1 Introduction 179
11.2 The Shipper Problem 180
11.2.1 The Shipper Model 181
xiv Contents
11.2.2 A Set-Partitioning Approach 182
11.2.3 Structural Properties 186
11.2.4 Solution Procedure 187
11.2.5 Computational Results 190
11.3 Safety Stock Optimization 194
11.4 Exercises 196
12 Facility Location Models 199
12.1 Introduction 199
12.2 An Algorithm for the p-Median Problem 200
12.3 An Algorithm for the Single-Source Capacitated Facility Location
Problem 204
12.4 A Distribution System Design Problem 207
12.5 The Structure of the Asymptotic Optimal Solution 212
12.6 Exercises 213
IV Vehicle Routing Models 215
13 The Capacitated VRP with Equal Demands 217
13.1 Introduction 217
13.2 Worst-Case Analysis of Heuristics 218
13.3 The Asymptotic Optimal Solution Value 223
13.4 Asymptotically Optimal Heuristics 225
13.5 Exercises 228
14 The Capacitated VRP with Unequal Demands 229
14.1 Introduction 229
14.2 Heuristics for the CVRP 229
14.3 Worst-Case Analysis of Heuristics 233
14.4 The Asymptotic Optimal Solution Value 236
14.4.1 A Lower Bound 237
14.4.2 An Upper Bound 240
14.5 Probabilistic Analysis of Classical Heuristics 242
14.5.1 A Lower Bound 244
14.5.2 The UOP(q) Heuristic 246
14.6 The Uniform Model 248
14.7 The Location-Based Heuristic 250
14.8 Rate of Convergence to the Asymptotic Value 253
14.9 Exercises 254
15 The VRP with Time Window Constraints 257
15.1 Introduction 257
15.2 The Model 257
15.3 The Asymptotic Optimal Solution Value 259
Contents xv
15.4 An Asymptotically Optimal Heuristic 265
15.4.1 The Location-Based Heuristic 265
15.4.2 A Solution Method for CVLPTW 267
15.4.3 Implementation 269
15.4.4 Numerical Study 269
15.5 Exercises 272
16 Solving the VRP Using a Column Generation Approach 275
16.1 Introduction 275
16.2 Solving a Relaxation of the Set-Partitioning Formulation 276
16.3 Solving the Set-Partitioning Problem 280
16.3.1 Identifying Violated Clique Constraints 282
16.3.2 Identifying Violated Odd Hole Constraints 282
16.4 The Effectiveness of the Set-Partitioning Formulation 283
16.4.1 Motivation 284
16.4.2 Proof of Theorem 8.4.1 285
16.5 Exercises 288
V Logistics Algorithms in Practice 292
17 Network Planning 293
17.1 Introduction 293
17.2 Network Design 294
17.3 Strategic Safety Stock 305
17.4 Resource Allocation 313
17.5 Summary 317
17.6 Exercises 318
18 A Case Study: School Bus Routing 319
18.1 Introduction 319
18.2 The Setting 320
18.3 Literature Review 322
18.4 The Problem in New York City 323
18.5 Distance and Time Estimation 325
18.6 The Routing Algorithm 327
18.7 Additional Constraints and Features 331
18.8 The Interactive Mode 333
18.9 Data, Implementation and Results 334
19 References 337
Index 350
List of Tables
9.1 Summary of Results and Tools for the Inventory (and Pricing) Prob¬
lems 157
9.2 Summary of Results for Risk Neutral and Risk Averse Models . . . 163
11.1 Test problems generated as in alakrishnan and Graves 191
11.2 Computational results for layered networks, alakrishnan and Graves
results (B G) versus those of our Linear Programming Based Heuris¬
tic (LPBH) 191
11.3 Computational results for general networks. Balakrishnan and Graves
results (B G) versus those of our Linear Programming Based Heuris¬
tic (LPBH) 192
11.4 Linear and set-up costs used for all the test problems 193
11.5 Inventory costs and different ranges for the different test problems. 194
11.6 Computational results for a single warehouse 195
17.1 Network Planning Characteristics 318
xvii
List of Figures
1.1 The Logistics Network 2
2.1 Examples of Convex Sets and Non-Convex Sets 14
2.2 Illustration of the Definition of Convex Function 15
2.3 Illustration of the Definition of Subgradient 19
3.1 An example for the minimum spanning tree based algorithm with
n = 18 43
3.2 An example for the nearest insertion algorithm with n = 8 46
3.3 The matching and the optimal traveling salesman tour 48
3.4 An example for Christofides algorithm with n = 7 48
4.1 The two traveling salesman tours constructed by the partitioning
algorithm 62
4.2 Region partitioning example with n = 17, q = 3, h = 2 and t = 1. . 63
4.3 The tour generated by the region partitioning algorithm 64
4.4 The segments Si,... ,Sk and the corresponding Eulerian graph. . . 65
6.1 Inventory level as a function of time 86
6.2 Inventory level as a function of time under policy V 88
7.1 The plotted points and the function g 109
8.1 Illustration of the Properties of/i -convex Functions 125
9.1 Illustration of the Properties of a symmetric A -convex Function . 150
10.1 Illustration of the structure of the optimal ordering policy 177
11.1 Example of expanded network 182
11.2 Piece-wise linear and concave cost structure 183
xix
xx List of Figures
11.3 Illustration of the model 196
13.1 Every group contains Q customers with interdistance zero 219
13.2 An optimal traveling salesman tour in Q(t, s) 219
13.3 Solution obtained by the ITP heuristic 222
17.1 The APS screen representing data prior to optimization 296
17.2 The APS screen representing the optimized logistics network . . . 296
17.3 The APS screen representing data prior to aggregation 298
17.4 The APS screen representing data after aggregation 299
17.5 Transportation Rates for Shipping 4000 lb 301
17.6 How to read the diagrams 307
17.7 Current Safety Stock Locations 308
17.8 Optimized Safety Stock Locations 308
17.9 Optimized Safety Stock with Reduced Lead Time 309
17.10 Current Supply Chain 310
17.11 Optimized Supply Chain 311
17.12 Global vs. local optimization 312
17.13 The extended Supply Chain: From manufacturing to order fulfillment.314
17.14 Comparison of manual versus optimized scenarios 317
|
| any_adam_object | 1 |
| author | Simchi-Levi, David Chen, Xin 1967- Bramel, Julien |
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| indexdate | 2024-12-20T12:02:12Z |
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| isbn | 0387221999 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-012958554 |
| oclc_num | 249629499 |
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| spellingShingle | Simchi-Levi, David Chen, Xin 1967- Bramel, Julien The logic of logistics theory, algorithms, and applications for logistics and supply chain management Business logistics Operations research Unternehmen (DE-588)4061963-1 gnd Logistik (DE-588)4036210-3 gnd Logistikplanung (DE-588)4246866-8 gnd Operations Research (DE-588)4043586-6 gnd |
| subject_GND | (DE-588)4061963-1 (DE-588)4036210-3 (DE-588)4246866-8 (DE-588)4043586-6 |
| title | The logic of logistics theory, algorithms, and applications for logistics and supply chain management |
| title_auth | The logic of logistics theory, algorithms, and applications for logistics and supply chain management |
| title_exact_search | The logic of logistics theory, algorithms, and applications for logistics and supply chain management |
| title_full | The logic of logistics theory, algorithms, and applications for logistics and supply chain management David Simchi-Levi ; Xin Chen ; Julien Bramel |
| title_fullStr | The logic of logistics theory, algorithms, and applications for logistics and supply chain management David Simchi-Levi ; Xin Chen ; Julien Bramel |
| title_full_unstemmed | The logic of logistics theory, algorithms, and applications for logistics and supply chain management David Simchi-Levi ; Xin Chen ; Julien Bramel |
| title_old | Bramel, Julien The logic of logistics |
| title_short | The logic of logistics |
| title_sort | the logic of logistics theory algorithms and applications for logistics and supply chain management |
| title_sub | theory, algorithms, and applications for logistics and supply chain management |
| topic | Business logistics Operations research Unternehmen (DE-588)4061963-1 gnd Logistik (DE-588)4036210-3 gnd Logistikplanung (DE-588)4246866-8 gnd Operations Research (DE-588)4043586-6 gnd |
| topic_facet | Business logistics Operations research Unternehmen Logistik Logistikplanung Operations Research |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012958554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT simchilevidavid thelogicoflogisticstheoryalgorithmsandapplicationsforlogisticsandsupplychainmanagement AT chenxin thelogicoflogisticstheoryalgorithmsandapplicationsforlogisticsandsupplychainmanagement AT brameljulien thelogicoflogisticstheoryalgorithmsandapplicationsforlogisticsandsupplychainmanagement |
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