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In the past decade, the study of how to apply genetic algorithms to problems in the industrial engineering world has been a subject engaging the curiosity of many researchers and practitioners in the area of management science, operations research, and industrial and systems engineering. A major reason for this interest is that genetic algorithms are powerful and broadly applicable stochastic search and optimization techniques that really work for many problems that are very difficult to solve by conventional techniques. Most engineering problems are optimization problems subject to complex constraints.
Simple genetic algorithms usually do not produce successful applications for these thorny engineering optimization problems. Therefore, a method to tailor genetic algorithms to meet the nature of these problems is one of the major focuses of research into industrial engineering-oriented genetic algorithms.
This book is intended to cover the major research topics of the application of genetic algorithms to industrial engineering optimization problems. Since the spring of 1993, we have devoted our efforts to the study of industrial engineering-oriented genetic algorithms. In our group, six students in the doctoral course have finished their studies on applying genetic algorithms to scheduling problems, spanning tree problems, transportation problems, reliability optimization problems, location and allocation problems, and production plan problems.
We summarized the results of related genetic algorithm studies from 1992 to early 1996 in our book Genetic Algorithms and Engineering Design (New York: John Wiley &amp Sons, 1997), including the following major topics: constrained optimization problems, combinatorial optimization problems, reliability optimization problems, flow-shop scheduling problems, job-shop scheduling problems, machine scheduling problems, transportation problems, facility layout design problems, and other topics in engineering design. The major motivations for writing this book are that due to size limitations, some interesting studies were not included in our first book, and also, some new and interesting results have been reported since then.
The book is suitable for a course in the genetic algorithms and applications at the upper-level undergraduate or beginning graduate level in industrial and system engineering, management science, operations researches, computer science, and related areas. The book is also useful as a comprehensive reference text for system analysts, operations researchers, management scientists, engineers, and other specialists who face challenging optimization problems inherent in industrial engineering/operations research.
Foundations of Genetic Algorithms
Combinatorial Optimization Problems
Mnltiobjective Optimization Problems
Fuzzy Optimization Problems
Reliability Design Problems
Scheduling Problems
Advanced Transportation Problems
Network Design and Routing
Manufacturing Cell Design