Network Models and Optimization (eBook)
XIV, 692 Seiten
Springer London (Verlag)
978-1-84800-181-7 (ISBN)
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.
Professor Mitsuo Gen is currently a professor of the Graduate School of Information, Production and Systems at Waseda University. He previously worked as a lecturer and professor at Ashikaga Institute of Technology. His research interests include genetic and evolutionary computation; fuzzy logic and neural networks; supply chain network design; optimization for information networks; and advanced planning and scheduling (APS).
Runwei Cheng is a Doctor of Engineering and currently works for JANA Solutions, Inc.
Lin Lin is currently a PhD candidate and research assistant at Waseda University, where he gained his MSc from the Graduate School of Information, Production and Systems. His research interests include hybrid genetic algorthims; neural networks; engineering optimization; multiobjective optimization; applications of evolutionary techniques; production and logistics; communication networks; image processing and pattern recognition; and parallel and distributed systems.
Network 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.
Professor Mitsuo Gen is currently a professor of the Graduate School of Information, Production and Systems at Waseda University. He previously worked as a lecturer and professor at Ashikaga Institute of Technology. His research interests include genetic and evolutionary computation; fuzzy logic and neural networks; supply chain network design; optimization for information networks; and advanced planning and scheduling (APS). Runwei Cheng is a Doctor of Engineering and currently works for JANA Solutions, Inc. Lin Lin is currently a PhD candidate and research assistant at Waseda University, where he gained his MSc from the Graduate School of Information, Production and Systems. His research interests include hybrid genetic algorthims; neural networks; engineering optimization; multiobjective optimization; applications of evolutionary techniques; production and logistics; communication networks; image processing and pattern recognition; and parallel and distributed systems.
Preface 6
Contents 10
1 Multiobjective Genetic Algorithms 16
1.1 Introduction 16
1.2 Implementation of Genetic Algorithms 20
1.3 Hybrid Genetic Algorithms 30
1.4 Multiobjective Genetic Algorithms 40
References 59
2 Basic Network Models 64
2.1 Introduction 64
2.2 Shortest Path Model 72
2.3 Minimum Spanning Tree Models 94
2.4 Maximum Flow Model 111
2.5 Minimum Cost Flow Model 122
2.6 Bicriteria MXF/MCF Model 130
2.7 Summary 143
References 145
3 Logistics Network Models 150
3.1 Introduction 150
3.2 Basic Logistics Models 154
3.3 Location Allocation Models 169
3.4 Multi-stage Logistics Models 190
3.5 Flexible Logistics Model 208
3.6 Integrated Logistics Model with Multi-time Period and Inventory 223
3.7 Summary 237
References 240
4 Communication Network Models 244
4.1 Introduction 244
4.2 Centralized Network Models 249
4.3 Backbone Network Model 261
4.4 Reliable Network Models 272
4.5 Summary 305
References 306
5 Advanced Planning and Scheduling Models 312
5.1 Introduction 312
5.2 Job-shop Scheduling Model 318
5.3 Flexible Job-shop Scheduling Model 352
5.4 Integrated Operation Sequence and Resource Selection Model 370
5.5 Integrated Scheduling Model with Multi-plant 391
5.6 Manufacturing and Logistics Model with Pickup and Delivery 410
5.7 Summary 427
References 427
6 Project Scheduling Models 434
6.1 Introduction 434
6.2 Resource-constrained Project Scheduling Model 436
6.3 Resource-constrained Multiple Project Scheduling Model 453
6.4 Resource-constrained Project Scheduling Model with Multiple Modes 472
6.5 Summary 487
References 488
7 Assembly Line Balancing Models 492
7.1 Introduction 492
7.2 Simple Assembly Line Balancing Model 495
7.3 U-shaped Assembly Line Balancing Model 508
7.4 Robotic Assembly Line Balancing Model 520
7.5 Mixed-model Assembly Line Balancing Model 541
7.6 Summary 561
References 561
8 Tasks Scheduling Models 566
8.1 Introduction 566
8.2 Continuous Task Scheduling 577
8.3 Real-time Task Scheduling in Homogeneous Multiprocessor 598
8.4 Real-time Task Scheduling in Heterogeneous Multiprocessor System 610
8.5 Summary 617
References 619
9 Advanced Network Models 622
9.1 Airline Fleet Assignment Models 622
9.2 Container Terminal Network Model 651
9.3 AGV Dispatching Model 666
9.4 Car Navigation Routing Model 681
9.5 Summary 696
References 697
Index 702
Erscheint lt. Verlag | 10.7.2008 |
---|---|
Reihe/Serie | Decision Engineering | Decision Engineering |
Zusatzinfo | XIV, 692 p. |
Verlagsort | London |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Mathematik / Informatik ► Mathematik | |
Technik ► Maschinenbau | |
Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation | |
Schlagworte | Algorithm analysis and problem complexity • algorithms • combinatorics • Engineering Economics • Genetic algorithms • logistics • Logistics Models • Manufacturing • Network Models • Operations Research • Optimization • Scheduling • Scheduling Models |
ISBN-10 | 1-84800-181-9 / 1848001819 |
ISBN-13 | 978-1-84800-181-7 / 9781848001817 |
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