1995 年 7 巻 5 号 p. 1073-1083
Job-shop Scheducling Problem(JSP) is one of extremely is one of extremely hard problems because it requires very large combinatorial search space and there are some precedence constraints between machines. The Genetic Algorithm(GA) is known as one of the most powerful tools for solving this kind of problems, especially it is more useful for large scale real-world problem.Generally, the data of real-world problems are imprecise, vague or uncertain. In this situation, we should estimate the input data with considering their uncertainty, and the uncertainty may be represented by a fuzzy number, and so reduce errors of imprecision.In this paper, we formulate fuzzy JSP and propose a new method for solving it after integrating GA in which processing time is represented by fuzzy number. We demonstrate its performance by the standard benchmark of job-shop scheduling problems with two different methods of ranking fuzzy subsets.
