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Metaheuristics for Logistics [Kõva köide]

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  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 241x164x19 mm, kaal: 481 g
  • Ilmumisaeg: 12-Feb-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848218087
  • ISBN-13: 9781848218086
Teised raamatud teemal:
Metaheuristics for LogisticsSuurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 241x164x19 mm, kaal: 481 g
  • Ilmumisaeg: 12-Feb-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848218087
  • ISBN-13: 9781848218086
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848218086.html
Märksõnad:

This book describes the main classical combinatorial problems that can be encountered when designing a logistics network or driving a supply chain. It shows how these problems can be tackled by metaheuristics, both separately and using an integrated approach. A huge number of techniques, from the simplest to the most advanced ones, are given for helping the reader to implement efficient solutions that meet its needs.

A lot of books have been written about metaheuristics (methods for solving hard optimization problems) and supply chain management (the field in which we find a huge number of combinatorial optimization problems) in the last decades. So, the main reason of this book is to describe how these methods can be implemented for this class of problems.

Introduction xi
Part 1 Basic Notions
1(68)
Chapter 1 Introductory Problems
3(10)
1.1 The "swing states" problem
3(2)
1.2 Adel and his camels
5(2)
1.3 Sauron's forges
7(6)
1.3.1 Problem 1: The inspection of the forges
8(1)
1.3.2 Problem 2: The production of the deadly weapon
9(4)
Chapter 2 A Review of Logistic Problems
13(24)
2.1 Some history
13(3)
2.1.1 The Fermat--Torricelli point
13(1)
2.1.2 The Monge problem
14(1)
2.1.3 The Seven Bridges of Konigsberg and the Icosian Game
15(1)
2.2 Some polynomial problems
16(4)
2.2.1 The assignment problem
16(1)
2.2.2 The transportation problem
17(2)
2.2.3 The Minimum-Cost Spanning Tree problem
19(1)
2.3 Packing problems
20(2)
2.3.1 The knapsack problem
20(1)
2.3.2 The bin packing problem
21(1)
2.4 Routing problems
22(2)
2.4.1 The traveling salesman problem
23(1)
2.4.2 The vehicle routing problem (VRP)
24(1)
2.5 Production scheduling problems
24(7)
2.5.1 The flow-shop scheduling problem (FSSP)
26(3)
2.5.2 The job-shop scheduling problem (JSSP)
29(2)
2.6 Lot-sizing problems
31(2)
2.7 Facility location problems
33(3)
2.7.1 The Uncapacitated Plant Location Problem (UPLP)
33(2)
2.7.2 The Dynamic Location Problem (DLP)
35(1)
2.8 Conclusion
36(1)
Chapter 3 An Introduction to Metaheuristics
37(20)
3.1 Optimization problems
37(2)
3.2 Metaheuristics: basic notions
39(2)
3.2.1 Intensification and diversification
40(1)
3.2.2 Neighborhood systems
40(1)
3.3 Individual-based metaheuristics
41(9)
3.3.1 Local search
41(3)
3.3.2 Simulated annealing
44(2)
3.3.3 The kangaroo Algorithm
46(2)
3.3.4 Iterated local search
48(1)
3.3.5 Tabu Search
49(1)
3.4 Population-based metaheuristics
50(5)
3.4.1 Evolutionary algorithms
51(1)
3.4.2 The ant colony algorithm
52(1)
3.4.3 Particle Swarm Optimization
53(2)
3.5 Conclusion
55(2)
Chapter 4 A First Implementation of Metaheuristics
57(12)
4.1 Representing a list of objects
57(2)
4.2 The implementation of a local search
59(5)
4.2.1 The construction of an initial solution
59(1)
4.2.2 Description of basic moves
60(2)
4.2.3 The implementation of stochastic descent (LS)
62(2)
4.3 The implementation of individual-based metaheuristics
64(2)
4.3.1 Simulated annealing (SA)
64(2)
4.3.2 Iterated local search (ILS)
66(1)
4.14 Conclusion
66(3)
Part 2 Advanced Notions
69(50)
Chapter 5 The Traveling Salesman Problem
71(18)
5.1 Representing a solution: the two-level tree structure
71(3)
5.2 Constructing initial solutions
74(4)
5.2.1 A greedy heuristic: nearest neighbor
74(2)
5.2.2 A simplification heuristic: the Christofides algorithm
76(2)
5.3 Neighborhood systems
78(8)
5.3.1 The Lin & Kernighan neighborhood
79(4)
5.3.2 Ejection chain techniques
83(3)
5.4 Some results
86(2)
5.5 Conclusion
88(1)
Chapter 6 The Flow-Shop Problem
89(20)
6.1 Representation and assessment of a solution
89(1)
6.2 Construction of the initial solution
90(7)
6.2.1 Simplification heuristics: CDS
91(3)
6.2.2 A greedy heuristic: NEH
94(3)
6.3 Neighborhood systems
97(10)
6.3.1 Improvement of the insertion movements
98(3)
6.3.2 Variable-depth neighborhood search
101(6)
6.4 Results
107(1)
6.5 Conclusion
107(2)
Chapter 7 Some Elements for Other Logistic Problems
109(10)
7.1 Direct representation versus indirect representation
109(2)
7.2 Conditioning problems
111(3)
7.2.1 The knapsack problem
111(1)
7.2.2 The bin-packing problem
112(2)
7.3 Lot-sizing problems
114(1)
7.4 Localization problems
115(2)
7.5 Conclusion
117(2)
Part 3 Evolutions and Current Trends
119(66)
Chapter 8 Supply Chain Management
121(10)
8.1 Introduction to supply chain management
121(1)
8.2 Horizontal synchronization of the supply chain
122(4)
8.2.1 The beer game
123(2)
8.2.2 The bullwhip effect
125(1)
8.3 Vertical synchronization of a supply chain
126(1)
8.4 An integral approach of the supply chain
127(2)
8.5 Conclusion
129(2)
Chapter 9 Hybridization and Coupling Using Metaheuristics
131(12)
9.1 Metaheuristics for the optimization of the supply chain
131(2)
9.2 Hybridization of optimization methods
133(5)
9.2.1 Classification of hybrid methods
133(1)
9.2.2 Illustration by example
134(1)
9.2.3 "Metaheuristic/local search" hybridization
135(1)
9.2.4 Metaheuristic hybridization/Exact Methods
135(3)
9.3 Coupling of optimization methods and performance evaluations
138(3)
9.3.1 Double complexity
138(1)
9.3.2 Coupling of optimization method/simulation model
139(2)
9.4 Conclusion
141(2)
Chapter 10 Flexible Manufacturing Systems
143(18)
10.1 Introduction to the FMS challenges
143(2)
10.2 The job-shop problem with transport
145(3)
10.2.1 Definition of the problem
145(3)
10.3 Proposal for a metaheuristic/simulation coupling
148(6)
10.3.1 Representation of a solution
148(1)
10.3.2 Simulation method
149(3)
10.3.3 Optimization method
152(1)
10.3.4 Results
153(1)
10.4 Workshop layout problem
154(5)
10.4.1 Aggregated model and exact resolution
154(3)
10.4.2 Detailed model and approximate solutions
157(2)
10.5 Conclusion
159(2)
Chapter 11 Synchronization Problems Based on Vehicle Routings
161(12)
11.1 Inventory routing problem
162(5)
11.1.1 Presentation of the problem
162(4)
11.1.2 Resolution by metaheuristics
166(1)
11.2 The location-routing problem
167(5)
11.2.1 Definition of the problem
167(4)
11.2.2 Solution with metaheuristics
171(1)
11.3 Conclusion
172(1)
Chapter 12 Solution to Problems
173(12)
12.1 The swing state problem
173(3)
12.2 Adel and his camels
176(4)
12.2.1 First question
176(1)
12.2.2 Second question
177(3)
12.2.3 Third question
180(1)
12.3 The forges of Sauron
180(5)
12.3.1 The inspection of the forges
180(3)
12.3.2 Production of the lethal weapon
183(2)
Conclusion 185(2)
Bibliography 187(10)
Index 197
Laurent Deroussi, Associate professor at the Blaise Pascal University of Clermont-Ferrand, France.