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Decision Engineering

Multiobjective Genetic Algorithm Approach

By: Mitsuo Gen, Runwei Cheng, Lin Lin

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Hardcover | 31 July 2008

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708 Pages

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22.86 x 15.24 x 3.81

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Network models are critical tools in business, management, science and industry. "Network Models and Optimization" 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. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling 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. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Engineering & Technology Technology in General Engineering in General Business & Management Operational Research Computer Science Artificial Intelligence Machine Learning

Mathematics Combinatorics & Graph Theory Management & Management Techniques

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Paperback

Published: 22nd October 2010

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Multiobjective Genetic Algorithms	p. 1
Introduction	p. 1
General Structure of a Genetic Algorithm	p. 2
Exploitation and Exploration	p. 3
Population-based Search	p. 4
Major Advantages	p. 4
Implementation of Genetic Algorithms	p. 5
GA Vocabulary	p. 5
Encoding Issue	p. 6
Fitness Evaluation	p. 10
Genetic Operators	p. 10
Handling Constraints	p. 13
Hybrid Genetic Algorithms	p. 15
Genetic Local Search	p. 16
Parameter Adaptation	p. 18
Multiobjective Genetic Algorithms	p. 25
Basic Concepts of Multiobjective Optimizations	p. 26
Features and Implementation of Multiobjective GA	p. 29
Fitness Assignment Mechanism	p. 30
Performance Measures	p. 41
References	p. 44
Basic Network Models	p. 49
Introduction	p. 49
Shortest Path Model: Node Selection and Sequencing	p. 50
Spanning Tree Model: Arc Selection	p. 51
Maximum Flow Model: Arc Selection and Flow Assignment	p. 52
Representing Networks	p. 53
Algorithms and Complexity	p. 54
NP-Complete	p. 55
List of NP-complete Problems in Network Design	p. 56
Shortest Path Model	p. 57
Mathematical Formulation of the SPP Models	p. 58
Priority-based GA for SPP Models	p. 60
Computational Experiments and Discussions	p. 72
Minimum Spanning Tree Models	p. 79
Mathematical Formulation of the MST Models	p. 83
PrimPred-based GA for MST Models	p. 85
Computational Experiments and Discussions	p. 96
Maximum Flow Model	p. 96
Mathematical Formulation	p. 99
Priority-based GA for MXF Model	p. 100
Experiments	p. 105
Minimum Cost Flow Model	p. 107
Mathematical Formulation	p. 108
Priority-based GA for MCF Model	p. 110
Experiments	p. 113
Bicriteria MXF/MCF Model	p. 115
Mathematical Formulations	p. 118
Priority-based GA for bMXF/MCF Model	p. 119
i-awGA for bMXF/MCF Model	p. 123
Experiments and Discussion	p. 125
Summary	p. 128
References	p. 130
Logistics Network Models	p. 135
Introduction	p. 135
Basic Logistics Models	p. 139
Mathematical Formulation of the Logistics Models	p. 139
Prüfer Number-based GA for the Logistics Models	p. 146
Numerical Experiments	p. 152
Location Allocation Models	p. 154
Mathematical Formulation of the Logistics Models	p. 156
Location-based GA for the Location Allocation Models	p. 159
Numerical Experiments	p. 170
Multi-stage Logistics Models	p. 175
Mathematical Formulation of the Multi-stage Logistics	p. 176
Priority-based GA for the Multi-stage Logistics	p. 185
Numerical Experiments	p. 190
Flexible Logistics Model	p. 193
Mathematical Formulation of the Flexible Logistics Model	p. 196
Direct Path-based GA for the Flexible Logistics Model	p. 202
Numerical Experiments	p. 206
Integrated Logistics Model with Multi-time Period and Inventory	p. 208
Mathematical Formulation of the Integrated Logistics Model	p. 210
Extended Priority-based GA for the Integrated Logistics Model	p. 213
Local Search Technique	p. 218
Numerical Experiments	p. 221
Summary	p. 222
References	p. 225
Communication Network Models	p. 229
Introduction	p. 229
Centralized Network Models	p. 234
Capacitated Multipoint Network Models	p. 235
Capacitated QoS Network Model	p. 242
Backbone Network Model	p. 246
Pierre and Legault's Approach	p. 248
Numerical Experiments	p. 252
Konak and Smith's Approach	p. 253
Numerical Experiments	p. 257
Reliable Network Models	p. 257
Reliable Backbone Network Model	p. 259
Reliable Backbone Network Model with Multiple Goals	p. 269
Bicriteria Reliable Network Model of LAN	p. 274
Bi-level Network Design Model	p. 283
Summary	p. 290
References	p. 291
Advanced Planning and Scheduling Models	p. 297
Introduction	p. 297
Job-shop Scheduling Model	p. 303
Mathematical Formulation of JSP	p. 304
Conventional Heuristics for JSP	p. 305
Genetic Representations for JSP	p. 316
Gen-Tsujimura-Kubota's Approach	p. 325
Cheng-Gen-Tsujimura's Approach	p. 326
Gonçalves-Magalhacs-Resende's Approach	p. 330
Experiment on Benchmark Problems	p. 335
Flexible Job-shop Scheduling Model	p. 337
Mathematical Formulation of fJSP	p. 338
Genetic Representations for fJSP	p. 340
Multistage Operation-based GA for fJSP	p. 344
Experiment on Benchmark Problems	p. 353
Integrated Operation Sequence and Resource Selection Model	p. 355
Mathematical Formulation of iOS/RS	p. 358
Multistage Operation-based GA for iOS/RS	p. 363
Experiment and Discussions	p. 372
Integrated Scheduling Model with Multi-plant	p. 376
Integrated Data Structure	p. 379
Mathematical Models	p. 381
Multistage Operation-based GA	p. 383
Numerical Experiment	p. 389
Manufacturing and Logistics Model with Pickup and Delivery	p. 395
Mathematical Formulation	p. 395
Multiobjective Hybrid Genetic Algorithm	p. 399
Numerical Experiment	p. 407
Summary	p. 412
References	p. 412
Project Scheduling Models	p. 419
Introduction	p. 419
Resource-constrained Project Scheduling Model	p. 421
Mathematical Formulation of rc-PSP Models	p. 422
Hybrid GA for rc-PSP Models	p. 426
Computational Experiments and Discussions	p. 434
Resource-constrained Multiple Project Scheduling Model	p. 438
Mathematical Formulation of rc-mPSP Models	p. 440
Hybrid GA for rc-mPSP Models	p. 444
Computational Experiments and Discussions	p. 451
Resource-constrained Project Scheduling Model with Multiple Modes	p. 457
Mathematical Formulation of rc-PSP/mM Models	p. 457
Adaptive Hybrid GA for rc-PSP/mM Models	p. 461
Numerical Experiment	p. 470
Summary	p. 472
References	p. 472
Assembly Line Balancing Models	p. 477
Introduction	p. 477
Simple Assembly Line Balancing Model	p. 480
Mathematical Formulation of sALB Models	p. 480
Priority-based GA for sALB Models	p. 484
Computational Experiments and Discussions	p. 492
U-shaped Assembly Line Balancing Model	p. 493
Mathematical Formulation of uALB Models	p. 495
Priority-based GA for uALB Models	p. 499
Computational Experiments and Discussions	p. 505
Robotic Assembly Line Balancing Model	p. 505
Mathematical Formulation of rALB Models	p. 509
Hybrid GA for rALB Models	p. 512
Computational Experiments and Discussions	p. 523
Mixed-model Assembly Line Balancing Model	p. 526
Mathematical Formulation of mALB Models	p. 529
Hybrid GA for mALB Models	p. 532
Rekiek and Delchambre's Approach	p. 542
Ozmehmet Tasan and Tunali's Approach	p. 543
Summary	p. 546
References	p. 546
Tasks Scheduling Models	p. 551
Introduction	p. 551
Hard Real-time Task Scheduling	p. 553
Soft Real-time Task Scheduling	p. 557
Continuous Task Scheduling	p. 562
Continuous Task Scheduling Model on Uniprocessor System	p. 563
Continuous Task Scheduling Model on Multiprocessor System	p. 575
Real-time Task Scheduling in Homogeneous Multiprocessor	p. 583
Soft Real-time Task Scheduling Problem (sr-TSP) and Mathematical Model	p. 584
Multiobjective GA for srTSP	p. 586
Numerical Experiments	p. 592
Real-time Task Scheduling in Heterogeneous Multiprocessor System	p. 595
Soft Real-time Task Scheduling Problem (sr-TSP) and Mathematical Model	p. 595
SA-based Hybrid GA Approach	p. 597
Numerical Experiments	p. 601
Summary	p. 602
References	p. 604
Advanced Network Models	p. 607
Airline Fleet Assignment Models	p. 607
Fleet Assignment Model with Connection Network	p. 613
Fleet Assignment Model with Time-space Network	p. 624
Container Terminal Network Model	p. 636
Berth Allocation Planning Model	p. 639
Multi-stage Decision-based GA	p. 643
Numerical Experiment	p. 646
AGV Dispatching Model	p. 651
Network Modeling and Mathematical Formulation	p. 652
Random Key-based GA	p. 658
Numerical Experiment	p. 664
Car Navigation Routing Model	p. 666
Data Analyzing	p. 667
Mathematical Formulation	p. 670
Improved Fixed Length-based GA	p. 672
Numerical Experiment	p. 677
Summary	p. 681
References	p. 682
Index	p. 687
Table of Contents provided by Publisher. All Rights Reserved.

Decision Engineering

Multiobjective Genetic Algorithm Approach

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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