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Genetic algorithms for minimizing makespan in a flow shop with two capacitated batch processing machines

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Abstract

This paper considers a flow shop with two batch processing machines. The processing times of the job and their sizes are given. The batch processing machines can process multiple jobs simultaneously in a batch as long as the total size of all the jobs in a batch does not exceed its capacity. When the jobs are grouped into batches, the processing time of the batch is defined by the longest processing job in the batch. Batch processing machines are expensive and a bottleneck. Consequently, the objective is to minimize the makespan (or maximize the machine utilization). The scheduling problem under study is NP-hard, hence, a genetic algorithm (GA) is proposed. The effectiveness (in terms of solution quality and run time) of the GA approach is compared with a simulated annealing approach, a heuristic, and a commercial solver which was used to solve a mixed-integer formulation of the problem. Experimental study indicates that the GA approach outperforms the other approaches by reporting better solution.

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Author information

Authors and Affiliations

  1. Cisco Systems Inc, Austin, TX, USA

    Praveen Kumar Manjeshwar

  2. Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL, USA

    Purushothaman Damodaran

  3. Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA

    Krishnaswami Srihari

Authors
  1. Praveen Kumar Manjeshwar
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  2. Purushothaman Damodaran
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  3. Krishnaswami Srihari
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Corresponding author

Correspondence to Purushothaman Damodaran.

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Manjeshwar, P.K., Damodaran, P. & Srihari, K. Genetic algorithms for minimizing makespan in a flow shop with two capacitated batch processing machines. Int J Adv Manuf Technol 55, 1171–1182 (2011). https://doi.org/10.1007/s00170-010-3150-0

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  • DOI: https://doi.org/10.1007/s00170-010-3150-0

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