Matheuristics: hybridizing metaheuristics and mathematical programming
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht [u.a.]
Springer
[2009]
|
| Schriftenreihe: | Annals of information systems
Volume 10 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 269 Seiten graphische Darstellungen |
| ISBN: | 9781441913050 |
Internformat
MARC
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| 245 | 1 | 0 | |a Matheuristics |b hybridizing metaheuristics and mathematical programming |c [Marco Caserta ...]. Edited by Vittorio Maniezzo ... |
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Datensatz im Suchindex
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| adam_text | Contents
1 Metaheuristics: Intelligent Problem Solving.................... 1
Marco Caserta and Stefan Vofi
1.1 Introduction............................................... 1
1.2 Basic Concepts and Discussion.............................. 5
1.2.1 Local Search....................................... 5
1.2.2 Metaheuristics..................................... 7
1.2.3 Miscellaneous..................................... 14
1.3 A Taxonomy................................................ 15
1.4 Hybrids with Exact Methods................................ 19
1.5 General Frames: A Pool-Template........................... 22
1.6 Fine Tuning and Evaluation of Algorithms ................. 24
1.6.1 Fine Tuning of Metaheuristics..................... 24
1.6.2 Empirical Evaluation of Metaheuristics............ 26
1.7 Optimization Software Libraries........................... 30
1.8 Conclusions .............................................. 30
References.................................................... 32 2
2 Just MIP it!.................................................. 39
Matteo Fischetti, Andrea Lodi, and Domenico Salvagnin
2.1 Introduction............................................ 40
2.2 MIP ping Cut Separation.................................. 41
2.2.1 Pure Integer Cuts................................. 43
2.2.2 Mixed Integer Cuts ............................... 44
2.2.3 A Computational Overview.......................... 47
2.3 MIPping Heuristics........................................ 50
2.3.1 Local Branching and Feasibility Pump.............. 51
2.3.2 LB with Infeasible Reference Solutions............ 54
2.3.3 Computational Results............................. 55
2.4 MIPping the Dominance Test................................ 61
2.4.1 Borrowing Nogoods from Constraint Programming . 63
2.4.2 Improving the Auxiliary Problem................... 64
IX
X
Contents
2.4.3 Computational Results........................... 65
References .................................................... 68
3 MetaBoosting: Enhancing Integer Programming
Techniques by Metaheuristics................................... 71
Jakob Puchinger, Günther R. Raidl, and Sandro Pirkwieser
3.1 Introduction............................................. 71
3.2 Integer Programming Techniques........................... 73
3.2.1 Relaxations and Duality........................... 73
3.2.2 LP-Based Branch-and-Bound......................... 75
3.2.3 Cutting Plane Algorithm and Branch-and-Cut..... 76
3.2.4 Column Generation and Branch-and-Price............ 77
3.3 Metaheuristics for Finding Primal Bounds................. 78
3.3.1 Initial Solutions................................. 78
3.3.2 B B Acting as Local Search Based Metaheuristic . . 80
3.3.3 Solution Merging.................................. 81
3.3.4 Metaheuristics and Lagrangian Relaxation.......... 83
3.4 Collaborative Hybrids ................................... 84
3.5 Metaheuristics for Cut and Column Generation............. 85
3.5.1 Cut Separation.................................... 85
3.5.2 Column Generation................................. 86
3.6 Case Study: A Lagrangian Decomposition/EA Hybrid...... 87
3.6.1 The Knapsack Constrained Maximum Spanning
Tree Problem...................................... 87
3.6.2 Lagrangian Decomposition of the KCMST Problem 88
3.6.3 Lagrangian Heuristic.............................. 89
3.6.4 Evolutionary Algorithm for the KCMST.............. 89
3.6.5 LD/EA Hybrid.................................... 90
3.6.6 Experimental Results.............................. 91
3.7 Case Study: Metaheuristic Column Generation.............. 92
3.7.1 The Periodic Vehicle Routing Problem with Time
Windows .......................................... 92
3.7.2 Set Covering Formulation for the PVRPTW........... 94
3.7.3 Column Generation for Solving the LP Relaxation . 95
3.7.4 Exact and Metaheuristic Pricing Procedures........ 96
3.7.5 Experimental Results.............................. 97
3.8 Conclusions ............................................. 99
References.....................................................100
4 Usage of Exact Algorithms to Enhance Stochastic Local
Search Algorithms..............................................103
Irina Dumitrescu and Thomas Stützle
4.1 Introduction........................................... 103
4.2 Exploring large neighborhoods............................106
4.2.1 NSP Example: Cyclic and Path Exchange
Neighborhoods
108
Contents
xi
4.2.2 NSP Example: Dynasearch...........................Ill
4.2.3 PNSP Example: Hyperopt Neighborhoods..............112
4.2.4 Other Approaches..................................113
4.2.5 Discussion .......................................114
4.3 Enhancing Metaheuristics.................................115
4.3.1 Example: Perturbation in Iterated Local Search .... 115
4.3.2 Other Approaches..................................117
4.3.3 Discussion .......................................118
4.4 Using Branch-and-Bound Techniques in Constructive
Search Heuristics........................................118
4.4.1 Example: Approximate Nondeterministic Tree
Search (ANTS)................................... 119
4.4.2 Other Approaches..................................121
4.5 Exploiting the Structure of Good Solutions...............121
4.5.1 Example: Heuristic Concentration..................122
4.5.2 Example: Tour Merging.............................123
4.5.3 Discussion........................................124
4.6 Exploiting Information from Relaxations in Metaheuristics . 125
4.6.1 Example: Simplex and Tabu Search Hybrid ..........125
4.6.2 Discussion .......................................127
4.7 Conclusions .............................................128
References....................................................129
5 Decomposition Techniques as Metaheuristic Frameworks . 135
Marco Boschetti, Vittorio Maniezzo, and Matteo Roffilli
5.1 Introduction.............................................135
5.2 Decomposition Methods....................................137
5.2.1 Lagrangean Relaxation.............................137
5.2.2 Dantzig-Wolfe Decomposition ......................138
5.2.3 Benders Decomposition.............................139
5.3 Metaheuristics Derived from Decompositions...............141
5.3.1 A Lagrangean Metaheuristic........................142
5.3.2 A Dantzig-Wolfe Metaheuristic.....................142
5.3.3 A Benders Metaheuristic...........................143
5.4 Single Source Capacitated Facility Location..............144
5.4.1 Solving the SCFLP with a Lagrangean
Metaheuristic.....................................146
5.4.2 Solving the SCFLP with a Dantzig-Wolfe
Metaheuristic.................................. 147
5.4.3 Solving the SCFLP with a Benders Metaheuristic . . 149
5.5 Computational Results....................................150
5.5.1 Lagrangean Metaheuristic..........................151
5.5.2 Dantzig-Wolfe Metaheuristic ......................153
5.5.3 Benders Metaheuristic.............................153
5.6 Conclusions .............................................155
References....................................................156
xii Contents
6 Convergence Analysis
of Metaheuristics............................................159
Walter J. Gutjahr
6.1 Introduction......................................... 159
6.2 A Generic Metaheuristic Algorithm......................161
6.3 Convergence............................................164
6.3.1 Convergence Notions.............................164
6.3.2 Best-So-Far Convergence.........................165
6.3.3 Model Convergence ..............................167
6.4 Proving Convergence....................................169
6.4.1 Proving Best-So-Far Convergence.................169
6.4.2 Proving Model Convergence..................... 169
6.5 Convergence for Problems with Noise....................175
6.6 Convergence Speed......................................178
6.7 Conclusions .......................................... 183
References...................................................184
7 MIP-based GRASP and Genetic Algorithm for Balancing
Transfer Lines...............................................189
Alexandre Dolgui, Anton Eremeev, and Olga Guschinskaya
7.1 Introduction...........................................189
7.2 Problem Statement......................................191
7.3 Greedy Randomized Adaptive Search Procedure ...........195
7.3.1 Construction Phase..............................195
7.3.2 Improvement Phase . . ..........................197
7.4 Genetic Algorithm......................................198
7.5 Experimental Results...................................200
7.5.1 Problem Instances ..............................200
7.5.2 Experimental Settings...........................201
7.5.3 Results.........................................202
7.6 Conclusions ...........................................206
References...................................................207
8 (Meta-)Heuristic Separation of Jump Cuts in a
Branch Cut Approach for the Bounded Diameter
Minimum Spanning Tree Problem................................209
Martin Gruber and Günther R. Raidl
8.1 Introduction...........................................209
8.2 Previous Work..........................................210
8.3 The Jump Model.........................................211
8.4 Jump Cut Separation....................................213
8.4.1 Exact Separation Model..........................214
8.4.2 Simple Construction Heuristic CA................215
8.4.3 Constraint Graph Based Construction Heuristic CB 216
8.4.4 Local Search and Tabu Search.......... . . ....219
8.5 Primal Heuristics......................................220
8.6 Computational Results..................................222
Contents
Xlll
g 7 Conclusions and Future Work.............................228
References.................................................. 228
9 A Good Recipe for Solving MINLPs..............................231
Leo Liberty Giacomo Nannicini, and Nenad Mladenovic
9.1 Introduction...........................................231
9.2 The Basic Ingredients...................................233
9.2.1 Variable Neighbourhood Search....................233
9.2.2 Local Branching..................................234
9.2.3 Branch-and-Bound for cMINLPs.....................234
9.2.4 Sequential Quadratic Programming ................235
9.3 The RECIPE Algorithm...................................236
9.3.1 Hyperrectangular Neighbourhood Structure.........236
9.4 Computational Results............................... 238
9.4.1 MINLPLib.........................................239
9.5 Conclusion..............................................242
References....................................................243
10 Variable Intensity Local Search...............................245
Snezana Mitrovic-Minic and Abraham P. Punnen
10.1 Introduction............................................245
10.2 The General VILS Framework..............................246
10.3 Experimental Studies....................................249
10.4 Conclusion..............................................250
References....................................................251
11 A Hybrid Tabu Search for the m-Peripatetic Vehicle
Routing Problem...............................................253
Sandra Ulrich Ngueveu, Christian Prins, and Roberto Wolfler
Calvo
11.1 Introduction............................................253
11.2 Tabu Search.............................................255
11.2.1 Initial Solution Heuristic and Neighborhood
Structure........................................255
11.2.2 Penalization and Tabu List Management........... . 257
11.3 Hybridization with 6-Matching and Diversification.......257
11.3.1 5-Matching.......................................257
11.3.2 Hybridization ...................................258
11.3.3 Diversification Procedure ..................... 259
11.4 Computational Analysis .................................259
11.4.1 VRP and ro-PSP ..................................261
11.4.2 m-PVRP with 2 m 7............................262
11.5 Conclusion..............................................263
References....................................................264
Index
267
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| spelling | Matheuristics hybridizing metaheuristics and mathematical programming [Marco Caserta ...]. Edited by Vittorio Maniezzo ... Dordrecht [u.a.] Springer [2009] XVII, 269 Seiten graphische Darstellungen txt rdacontent n rdamedia nc rdacarrier Annals of information systems Volume 10 Optimierung (DE-588)4043664-0 gnd rswk-swf Metaheuristik (DE-588)4820176-5 gnd rswk-swf Metaheuristik (DE-588)4820176-5 s Optimierung (DE-588)4043664-0 s DE-604 Maniezzo, Vittorio ca. 20./21. Jh. (DE-588)1175111791 edt Caserta, Marco Sonstige oth Erscheint auch als Online-Ausgabe 978-1-4419-1306-7 Annals of information systems Volume 10 (DE-604)BV022718881 10 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028473788&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Matheuristics hybridizing metaheuristics and mathematical programming Annals of information systems Optimierung (DE-588)4043664-0 gnd Metaheuristik (DE-588)4820176-5 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4820176-5 |
| title | Matheuristics hybridizing metaheuristics and mathematical programming |
| title_auth | Matheuristics hybridizing metaheuristics and mathematical programming |
| title_exact_search | Matheuristics hybridizing metaheuristics and mathematical programming |
| title_full | Matheuristics hybridizing metaheuristics and mathematical programming [Marco Caserta ...]. Edited by Vittorio Maniezzo ... |
| title_fullStr | Matheuristics hybridizing metaheuristics and mathematical programming [Marco Caserta ...]. Edited by Vittorio Maniezzo ... |
| title_full_unstemmed | Matheuristics hybridizing metaheuristics and mathematical programming [Marco Caserta ...]. Edited by Vittorio Maniezzo ... |
| title_short | Matheuristics |
| title_sort | matheuristics hybridizing metaheuristics and mathematical programming |
| title_sub | hybridizing metaheuristics and mathematical programming |
| topic | Optimierung (DE-588)4043664-0 gnd Metaheuristik (DE-588)4820176-5 gnd |
| topic_facet | Optimierung Metaheuristik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028473788&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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