Scheduling grouped jobs on parallel machines with soft computing

Since Zade introduced fuzzy theories into solving combinatorial problems, soft computing methods which consist of fuzzy theories and intelligent optimal algorithms have done many good jobs in setting complex combinatorial problems. This paper addresses a job scheduling model of M identical machines in parallel. The model assumes that a set-up time is incurred when a machine changes from processing one type of parts to a different type of parts. And the scheduling objective is to minimize the sum of total flow time. In this paper, the method of soft computing, which is Genetic Algorithm inserted with fuzzy logic operation has been tried to be used to set this kind of complicated combination optimal problem. The efficiency of this approach is tested on several groups of random problems and shows that soft computing has potential for practical application in larger scale production systems.