## Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems. |

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### Contents

523 Genetic Representations for JSP | 316 |

524 GenTsujimuraKubotas Approach | 325 |

525 ChengGenTsujimuras Approach | 326 |

526 GoncalvesMagalhacsResendes Approach | 330 |

527 Experiment on Benchmark Problems | 335 |

53 Flexible Jobshop Scheduling Model | 337 |

531 Mathematical Formulation of fJSP | 338 |

532 Genetic Representations for fJSP | 340 |

15 | |

16 | |

18 | |

25 | |

141 Basic Concepts of Multiobjective Optimizations | 26 |

142 Features and Implementation of Multiobjective GA | 29 |

143 Fitness Assignment Mechanism | 30 |

144 Performance Measures | 41 |

References | 44 |

Basic Network Models | 49 |

Node Selection and Sequencing | 50 |

Arc Selection | 51 |

Arc Selection and Flow Assignment | 52 |

214 Representing Networks | 53 |

215 Algorithms and Complexity | 54 |

216 NPComplete | 55 |

217 List of NPcomplete Problems in Network Design | 56 |

22 Shortest Path Model | 57 |

221 Mathematical Formulation of the SPP Models | 58 |

222 Prioritybased GA for SPP Models | 60 |

223 Computational Experiments and Discussions | 72 |

23 Minimum Spanning Tree Models | 79 |

231 Mathematical Formulation of the MST Models | 83 |

232 PrimPredbased GA for MST Models | 85 |

233 Computational Experiments and Discussions | 96 |

241 Mathematical Formulation | 99 |

242 Prioritybased GA for MXF Model | 100 |

243 Experiments | 105 |

25 Minimum Cost Flow Model | 107 |

251 Mathematical Formulation | 108 |

252 Prioritybased GA for MCF Model | 110 |

253 Experiments | 113 |

26 Bicriteria MXFMCF Model | 115 |

261 Mathematical Formulations | 118 |

262 Prioritybased GAfor bMXFMCF Model | 119 |

263 iawGA for bMXFMCF Model | 123 |

264 Experiments and Discussion | 125 |

27 Summary | 128 |

References | 130 |

Logistics Network Models | 135 |

32 Basic Logistics Models | 139 |

322 Prufer Numberbased GAfor the Logistics Models | 146 |

323 Numerical Experiments | 152 |

33 Location Allocation Models | 154 |

331 Mathematical Formulation of the Logistics Models | 156 |

332 Locationbased GAfor the Location Allocation Models | 159 |

333 Numerical Experiments | 170 |

34 Multistage Logistics Models | 175 |

341 Mathematical Formulation of the Multistage Logistics | 176 |

342 Prioritybased GAfor the Multistage Logistics | 185 |

343 Numerical Experiments | 190 |

35 Flexible Logistics Model | 193 |

351 Mathematical Formulation of the Flexible Logistics Model | 196 |

352 Direct Pathbased GAfor the Flexible Logistics Model | 202 |

353 Numerical Experiments | 206 |

36 Integrated Logistics Model with Multitime Period and Inventory | 208 |

361 Mathematical Formulation of the Integrated Logistics Model | 210 |

362 Extended Prioritybased GA for the Integrated Logistics Model | 213 |

363 Local Search Technique | 218 |

364 Numerical Experiments | 221 |

37 Summary | 222 |

References | 225 |

Communication Network Models | 229 |

42 Centralized Network Models | 234 |

421 Capacitated Multipoint Network Models | 235 |

422 Capacitated QoS Network Model | 242 |

43 Backbone Network Model | 246 |

431 Pierre and Legaults Approach | 248 |

432 Numerical Experiments | 252 |

433 Konak and Smiths Approach | 253 |

434 Numerical Experiments | 257 |

441 Reliable Backbone Network Model | 259 |

442 Reliable Backbone Network Model with Multiple Goals | 269 |

443 Bicriteria Reliable Network Model of LAN | 274 |

444 Bilevel Network Design Model | 283 |

45 Summary | 290 |

References | 291 |

Advanced Planning and Scheduling Models | 297 |

52 Jobshop Scheduling Model | 303 |

521 Mathematical Formulation of JSP | 304 |

522 Conventional Heuristics for JSP | 305 |

533 Multistage Operationbased GA for fJSP | 344 |

534 Experiment on Benchmark Problems | 353 |

54 Integrated Operation Sequence and Resource Selection Model | 355 |

541 Mathematical Formulation ofiOSRS | 358 |

542 Multistage Operationbased GA for iOSRS | 363 |

543 Experiment and Discussions | 372 |

55 Integrated Scheduling Model with Multiplant | 376 |

551 Integrated Data Structure | 379 |

552 Mathematical Models | 381 |

553 Multistage Operationbased GA | 383 |

554 Numerical Experiment | 389 |

56 Manufacturing and Logistics Model with Pickup and Delivery | 395 |

562 Multiobjective Hybrid Genetic Algorithm | 399 |

563 Numerical Experiment | 407 |

57 Summary | 412 |

Project Scheduling Models | 419 |

62 Resourceconstrained Project Scheduling Model | 421 |

621 Mathematical Formulation of rcPSP Models | 422 |

622 Hybrid GA for rcPSP Models | 426 |

623 Computational Experiments and Discussions | 434 |

63 Resourceconstrained Multiple Project Scheduling Model | 438 |

631 Mathematical Formulation ofrcmPSP Models | 440 |

632 Hybrid GA for rcmPSP Models | 444 |

633 Computational Experiments and Discussions | 451 |

64 Resourceconstrained Project Scheduling Model with Multiple Modes | 457 |

642 Adaptive Hybrid GA for rcPSPmM Models | 461 |

643 Numerical Experiment | 470 |

65 Summary | 472 |

References | 473 |

Assembly Line Balancing Models | 477 |

72 Simple Assembly Line Balancing Model | 480 |

722 Prioritybased GAfor sALB Models | 484 |

723 Computational Experiments and Discussions | 492 |

73 Ushaped Assembly Line Balancing Model | 493 |

731 Mathematical Formulation ofuALB Models | 495 |

732 Prioritybased GAfor uALB Models | 499 |

733 Computational Experiments and Discussions | 505 |

741 Mathematical Formulation of rALB Models | 509 |

742 Hybrid GA for rALB Models | 512 |

743 Computational Experiments and Discussions | 523 |

75 Mixedmodel Assembly Line Balancing Model | 526 |

751 Mathematical Formulation of mALB Models | 529 |

752 Hybrid GAfor mALB Models | 532 |

753 Rekiek and Delchambres Approach | 542 |

754 Ozmehmet Tasan and Tunalis Approach | 543 |

76 Summary | 546 |

Tasks Scheduling Models | 551 |

811 Hard Realtime Task Scheduling | 553 |

812 Soft Realtime Task Scheduling | 557 |

82 Continuous Task Scheduling | 562 |

821 Continuous Task Scheduling Model on Uniprocessor System | 563 |

822 Continuous Task Scheduling Model on Multiprocessor System | 575 |

83 Realtime Task Scheduling in Homogeneous Multiprocessor | 583 |

831 Soft Realtime Task Scheduling Problem srTSP and Mathematical Model | 584 |

832 Multiobjective GA for srTSP | 586 |

833 Numerical Experiments | 592 |

84 Realtime Task Scheduling in Heterogeneous Multiprocessor System | 595 |

842 SAbased Hybrid GA Approach | 597 |

843 Numerical Experiments | 601 |

85 Summary | 602 |

References | 604 |

Advanced Network Models | 607 |

911 Fleet Assignment Model with Connection Network | 613 |

912 Fleet Assignment Model with Timespace Network | 624 |

92 Container Terminal Network Model | 636 |

921 Berth Allocation Planning Model | 639 |

922 Multistage Decisionbased GA | 643 |

923 Numerical Experiment | 646 |

93 AGV Dispatching Model | 651 |

931 Network Modeling and Mathematical Formulation | 652 |

932 Random Keybased GA | 658 |

933 Numerical Experiment | 664 |

94 Car Navigation Routing Model | 666 |

941 Data Analyzing | 667 |

942 Mathematical Formulation | 670 |

943 Improved Fixed Lengthbased GA | 672 |

944 Numerical Experiment | 677 |

95 Summary | 681 |

References | 682 |

687 | |

### Other editions - View all

Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |

Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |