Solving the Job Shop Scheduling Problem with a Parallel and Agent-based Local Search Genetic Algorithm
نویسنده
چکیده
Job shop scheduling problems play an important role in both manufacturing systems and industrial process for improving the utilization of resources, and therefore it is crucial to develop efficient scheduling technologies. The Job shop scheduling problem, one of the best known production scheduling problems, has been proved to be NP-hard. In this paper, we present a parallel and agent-based local search genetic algorithm for solving the job shop scheduling problem. A multi agent system containing various agents each with special behaviors is developed to implement the parallel local search genetic algorithm. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed agent-based parallel local search genetic algorithm improves the efficiency.
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