1995 年 10 巻 5 号 p. 769-777
The job-shop scheduling problem (JSSP) is one of the most difficult problem to solve using various methods. We describe a GA approach which provides a better solution of JSSP in this paper. In GA, all individuals of population simultaneously search the best solution, and each individual is evaluated by its fitness in the environment. Originally, the evaluation is independent of genetic operations, such as crossover and mutation. So we introduce an interpreter which interprets the representation on a chromosome (genotype) and synthesizes a solution such as Gantt chart (phenotype). Then we can treat many complex constraints as procedures in the interpreter, and we solve a scheduling problem in a real world application. Along the line, We find that the solution based on the simple sequential coding is better than previous efforts using GA, and introducing "cluster averaging method (CAM)," which makes each cluster consist of same number of individuals, it improves the solution. The simple sequential coding represents an order that we allocate a work of a job on a Gantt chart. It means that the predecessor gene will determine the performance of succeccor genes. In other words, the head gene has the most important role in JSSP. Then we can introduce the schema that the only head gene of a chromosome has an fixed value and other genes have not-care values. Each individual is divided into those clusters by schema. Using the simple sequential coding with CAM, we can get better result in JSSP by experiment.
