Performance of Genetic Algorithms for Solving Flexible Job-shop Scheduling Problem
نویسندگان
چکیده
A Job-Shop Scheduling is a process–organized manufacturing facility. Its main characteristics are that a great diversity of jobs is performed. A Job-Shop produces goods (parts) and these parts have one or more alternatives process plans. Each process plan consists of a sequence of a operations and these operations require resources and have certain (predefined) duration on machines. The Job-Shop Scheduling is a problem of planning and organization of a set of tasks to be performed on a set of resources with variable performance. In this paper, two approaches Jobs Sequencing List Oriented Genetic Algorithm and Operations machines Coding Oriented Genetic Algorithm have been implemented and compared for solution of the Job-Shop scheduling problem. Each approach has its own coding, evaluation function, crossovers and mutations applicable in Job-Shop scheduling problem to minimize the makespan, the workload of the most loaded machine and the total workload of the machines. Jobs Sequencing List Oriented Genetic Algorithm has been found to be the best out of two approaches to minimize the objectives.
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