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E-raamat: Generalized Network Design Problems: Modeling and Optimization [De Gruyter e-raamatud]

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  • De Gruyter e-raamatud
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Teised raamatud teemal:
Combinatorial optimization is a fascinating topic. Combinatorial optimization problems arise in a wide variety of important fields such as transportation, telecommunications, computer networking, location, planning, distribution problems, etc. Important and significant results have been obtained on the theory, algorithms and applications over the last few decades. In combinatorial optimization, many network design problems can be generalized in a natural way by considering a related problem on a clustered graph, where the original problem's feasibility constraints are expressed in terms of the clusters, i.e., node sets instead of individual nodes. This class of problems is usually referred to as generalized network design problems (GNDPs) or generalized combinatorial optimization problems.

The express purpose of this monograph is to describe a series of mathematical models, methods, propositions, algorithms developed in the last years on generalized network design problems in a unified manner. The book consists of seven chapters, where in addition to an introductory chapter, the following generalized network design problems are formulated and examined: the generalized minimum spanning tree problem, the generalized traveling salesman problem, the railway traveling salesman problem, the generalized vehicle routing problem, the generalized fixed-charge network design problem and the generalized minimum vertex-biconnected network problem.

The book will be useful for researchers, practitioners, and graduate students in operations research, optimization, applied mathematics and computer science. Due to the substantial practical importance of some presented problems, researchers in other areas will find this book useful, too.
Preface v
1 Introduction
1(8)
1.1 Combinatorial optimization and integer programming
1(2)
1.2 Complexity theory
3(2)
1.3 Heuristic and relaxation methods
5(2)
1.4 Generalized network design problems
7(2)
2 The Generalized Minimum Spanning Tree Problem (GMSTP)
9(51)
2.1 Definition and complexity of the GMSTP
10(2)
2.2 An exact algorithm for the GMSTP
12(2)
2.3 Mathematical models of the GMSTP
14(14)
2.3.1 Formulations based on tree properties
14(3)
2.3.2 Formulations based on arborescence properties
17(2)
2.3.3 Flow based formulations
19(4)
2.3.4 A model based on Steiner tree properties
23(1)
2.3.5 Local-global formulation of the GMSTP
24(4)
2.4 Approximation results for the GMSTP
28(13)
2.4.1 Introduction
29(1)
2.4.2 Positive results: the design of the approximation algorithms
30(2)
2.4.3 A negative result for the GMSTP
32(2)
2.4.4 An approximation algorithm for the GMSTP with bounded cluster size
34(7)
2.5 Solving the GMSTP
41(18)
2.5.1 A branch-and-cut algorithm for solving the GMSTP
42(2)
2.5.2 A heuristic algorithm for solving the GMSTP
44(2)
2.5.3 Rooting procedure for solving the GMSTP
46(2)
2.5.4 Solving the GMSTP with Simulated Annealing
48(11)
2.6 Notes
59(1)
3 The Generalized Traveling Salesman Problem (GTSP)
60(40)
3.1 Definition and complexity of the GTSP
61(1)
3.2 An efficient transformation of the GTSP into the TSP
61(2)
3.3 An exact algorithm for the Generalized Traveling Salesman Problem
63(2)
3.4 Integer programming formulations of the GTSP
65(6)
3.4.1 Formulations based on the properties of Hamiltonian tours
65(1)
3.4.2 Flow based formulations
66(3)
3.4.3 A local-global formulation
69(2)
3.5 Solving the Generalized Traveling Salesman Problem
71(21)
3.5.1 Reinforcing ant colony system for solving the GTSP
72(3)
3.5.2 Computational results
75(3)
3.5.3 A hybrid heuristic approach for solving the GTSP
78(10)
3.5.4 Computational results
88(4)
3.6 The drilling problem
92(7)
3.6.1 Stigmergy and autonomous robots
93(1)
3.6.2 Sensitive robots
94(1)
3.6.3 Sensitive robot metaheuristic for solving the drilling problem
94(3)
3.6.4 Numerical experiments
97(2)
3.7 Notes
99(1)
4 The Railway Traveling Salesman Problem (RTSP)
100(28)
4.1 Definition of the RTSP
100(1)
4.2 Preliminaries
101(2)
4.3 Methods for solving the RTSP
103(18)
4.3.1 The size reduction method through shortest paths
103(8)
4.3.2 A cutting plane approach for the RTSP
111(3)
4.3.3 Solving the RTSP via a transformation into the classical TSP
114(5)
4.3.4 An ant-based heuristic for solving the RTSP
119(2)
4.4 Dynamic Railway Traveling Salesman Problem
121(5)
4.4.1 Ant colony approach to the Dynamic RTSP
122(2)
4.4.2 Implementation details and computational results
124(2)
4.5 Notes
126(2)
5 The Generalized Vehicle Routing Problem (GVRP)
128(39)
5.1 Definition and complexity of the GVRP
129(1)
5.2 An efficient transformation of the GVRP into a capacitated arc routing problem
130(2)
5.3 Integer linear programming formulations of the GVRP
132(7)
5.3.1 A general formulation
132(3)
5.3.2 A node based formulation
135(2)
5.3.3 Flow based formulations
137(2)
5.4 A numerical example
139(2)
5.5 Special cases of the proposed formulations
141(6)
5.5.1 The Generalized multiple Traveling Salesman Problem
141(2)
5.5.2 The Generalized Traveling Salesman Problem
143(1)
5.5.3 The Clustered Generalized Vehicle Routing Problem
144(3)
5.6 Solving the Generalized Vehicle Routing Problem
147(18)
5.6.1 An improved hybrid algorithm for Solving the GVRP
147(7)
5.6.2 An efficient memetic algorithm for solving the GVRP
154(5)
5.6.3 Computational experiments
159(6)
5.7 Notes
165(2)
6 The Generalized Fixed-Charge Network Design Problem (GFCNDP)
167(11)
6.1 Definition of the GFCNDP
168(1)
6.2 Integer programming formulations of the GFCNDP
169(4)
6.3 Solving the GFCNDP
173(2)
6.4 Computational results
175(2)
6.5 Notes
177(1)
7 The Generalized Minimum Edge-Biconnected Network Problem (GMEBCNP)
178(10)
7.1 Definition and complexity of the GMEBCNP
178(1)
7.2 A mixed integer programming model of the GMEBCNP
179(2)
7.3 Solving the GMEBCNP
181(4)
7.3.1 Simple Node Optimization Neighborhood (SNON)
181(1)
7.3.2 Node Re-Arrangement Neighborhood (NRAN)
182(1)
7.3.3 Edge Augmentation Neighborhood
182(1)
7.3.4 Node Exchange Neighborhood
183(1)
7.3.5 Variable Neighborhood Descent
183(2)
7.4 Computational results
185(1)
7.5 Notes
185(3)
Bibliography 188(14)
Index 202
Petrica C. Pop, North University of Baia Mare, Romania.