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A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

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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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Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Technology Madras, Chennai, 600 036, India

    T. Pasupathy & Chandrasekharan Rajendran

  2. Department of Production Engineering, Amrita Institute of Technology and Science, Ettimadi, Coimbatore, 641 105, India

    R.K. Suresh

Authors
  1. T. Pasupathy
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  2. Chandrasekharan Rajendran
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  3. R.K. Suresh
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Correspondence to Chandrasekharan Rajendran.

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Pasupathy, T., Rajendran, C. & Suresh, R. A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs. Int J Adv Manuf Technol 27, 804–815 (2006). https://doi.org/10.1007/s00170-004-2249-6

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  • DOI: https://doi.org/10.1007/s00170-004-2249-6

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