A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs |
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Authors: | T. Pasupathy Chandrasekharan Rajendran R.K. Suresh |
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Affiliation: | (1) Department of Management Studies, Indian Institute of Technology Madras, Chennai, 600 036, India;(2) Department of Production Engineering, Amrita Institute of Technology and Science, Ettimadi, Coimbatore, 641 105, India |
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Abstract: | In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS. |
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Keywords: | Flow shop scheduling Makespan Multi-objective genetic algorithms Total flow time |
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