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Improved solutions for job shop scheduling problems through genetic algorithm with a different method of schedule deduction

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Abstract

Job shop scheduling problems are one of the challenging combinatorial problems that have drawn the attention of researchers for the last three decades. It is observed that genetic algorithm (GA) is gaining more importance over the past several years. An attempt has been made through GA to solve job shop scheduling problems with job-based, operation-based, and proposed methods of representation and schedule deduction with the make-span objective. Computational experiments of this attempt have yielded better solutions coupled with appreciable reduction in computer processing time. A set of selected benchmark problems have been used with the proposed heuristic for validation and the results show the better performance of the proposed method of representation of jobs and schedule deduction.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Government College of Technology, Coimbatore, India

    K.S. Amirthagadeswaran

  2. Department of Mechanical Engineering, Government College of Technology, Coimbatore, India

    V.P. Arunachalam

Authors
  1. K.S. Amirthagadeswaran
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  2. V.P. Arunachalam
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Correspondence to K.S. Amirthagadeswaran.

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Amirthagadeswaran, K., Arunachalam, V. Improved solutions for job shop scheduling problems through genetic algorithm with a different method of schedule deduction. Int J Adv Manuf Technol 28, 532–540 (2006). https://doi.org/10.1007/s00170-004-2403-1

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  • DOI: https://doi.org/10.1007/s00170-004-2403-1

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