Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness.
The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.

Autorentext

Masatoshi Sakawa was born in Matsuyama, Japan, on 11 August l947. He received B.E., M.E., and D.E. degrees in applied mathematics and physics at Kyoto University in 1970, 1972, and 1975, respectively. From 1975 he was with Kobe University where, since 1981, he was an Associate Professor in the Department of Systems Engineering. From l987 to 1990 he was a Professor in the Department of Computer Science at Iwate University. At present he is a Professor at Hiroshima University and is working with the Department of Arti cial Complex Systems Engineering in the Graduate School of Engineering. He was an Honorary Visiting Professor at University of Manchester Institute of Science and Technology (UMIST), Computation Department, sponsored by the Japan Society for the Promotion of Science (JSPS) from March to December 1991. He was also a Visiting Professor at the Kyoto Institute of Economic Research, Kyoto University from April l991 to March l992. His research and teaching activities are in the area of systems engineering, especially mathematical optimization, multiobjective decision making, fuzzy mathematical programming and game theory. In addition to over 300 articles in National and International Journals, he is an author and coauthor of 6 books in English and 14 books in Japanese, including the Springer titles Genetic Algorithms and Fuzzy Multiobjective Optimization; Fuzzy Sets and Interactive Multiobjective Optimization; Large-Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches; and, with Nishizaki, Fuzzy and Multiobjective Games for Conflict Resolution. Hitoshi Yano is with the Department of Social Sciences, School of Humanities and Social Sciences, Nagoya City University. Ichiro Nishizaki received B.E. and M.E. degrees in systems engineering at Kobe University in 1982 and 1984, respectively, and he received the D.E. degree from Hiroshima University in 1993. From 1984 to 1990, he worked for Nippon SteelCorporation. From 1990 to 1993, he was a Research Associate at the Kyoto Institute of Economic Research, Kyoto University. From 1993 to 1996, he was an Associate Professor in the Faculty of Business Administration and Informatics at Setsunan University. From 1997 to 2001, he was an Associate Professor at Hiroshima University, and was working with the Department of Artificial Complex Systems Engineering in the Graduate School of Engineering. At present, he is a Professor in that department. His research and teaching activities are in the area of systems engineering, especially game theory, multiobjective decision making, and fuzzy mathematical programming. He is an author or coauthor of about eighty papers, one book in English (Springer: "Fuzzy and Multiobjective Games for Conflict Resolution"), and two books in Japanese.

Inhalt

1. Introduction.- 1.1 Introduction and historical remarks.- 1.2 Organization of the book.- 2. Foundations of Genetic Algorithms.- 2.1 Outline of genetic algorithms.- 2.2 Coding, fitness, and genetic operators.- 3. Genetic Algorithms for 01 Programming.- 3.1 Introduction.- 3.2 Multidimensional 01 knapsack problems.- 3.3 01 programming.- 3.4 Conclusion.- 4. Fuzzy Multiobjective 01 Programming.- 4.1 Introduction.- 4.2 Fuzzy multiobjective 01 programming.- 4.3 Fuzzy multiobjective 01 programming with fuzzy numbers.- 4.4 Conclusion.- 5. Genetic Algorithms for Integer Programming.- 5.1 Introduction.- 5.2 Multidimensional integer knapsack problems.- 5.3 Integer programming.- 5.4 Conclusion.- 6. Fuzzy Multiobjective Integer Programming.- 6.1 Introduction.- 6.2 Fuzzy multiobjective integer programming.- 6.3 Fuzzy multiobjective integer programming with fuzzy numbers.- 6.4 Conclusion.- 7. Genetic Algorithms for Nonlinear Programming.- 7.1 Introduction.- 7.2 Floating-point genetic algorithms.- 7.3 GENOCOP III.- 7.4 Revised GENOCOP III.- 7.5 Conclusion.- 8. Fuzzy Multiobjective Nonlinear Programming.- 8.1 Introduction.- 8.2 Multiobjective nonlinear programming.- 8.3 Multiobjective nonlinear programming problem with fuzzy numbers.- 8.4 Conclusion.- 9. Genetic Algorithms for Job-Shop Scheduling.- 9.1 Introduction.- 9.2 Job-shop scheduling.- 9.3 Genetic algorithms for job-shop scheduling.- 10.Fuzzy Multiobjective Job-Shop Scheduling.- 10.1 Introduction.- 10.2 Job-shop scheduling with fuzzy processing time and fuzzy due date.- 10.3 Multiobjective job-shop scheduling under fuzziness.- 11.Some Applications.- 11.1 Flexible scheduling in a machining center.- 11.2 Operation planning of district heating and cooling plants.- 11.3 Coal purchase planning in electric powerplants.- References.