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Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Author

Listed:
  • K. Raja
  • C. Arumugam
  • V. Selladurai

Abstract

Manufacturing industries frequently face the problem of reducing earliness and tardiness penalties, owing to the emerging concept of the just-in-time production philosophy. The problem studied in this work is the Non-identical Parallel-machine–Earliness-Tardiness Non-common Due Date–Sequence-dependent Set-up Time Scheduling Problem (NPETNDDSP) for jobs with varying processing times, where the objective is to minimise the sum of the absolute deviations of job completion times from their corresponding due dates for the different weighted earliness and tardiness combinations. A Genetic Algorithm-Fuzzy Logic Approach (GA-Fuzzy) has been proposed to select the optimal weighted earliness-tardiness combinations in a non-identical parallel-machine environment. The performance of the combined objective function obtained by the proposed GA-Fuzzy technique has been compared with the solutions yielded by the Genetic Algorithm (GA) techniques available in literature, known as GA with Partially Mapped Crossover Operator (GA-PMX) and GA with Multi-Component Uniform Order-based Crossover Generator (GA-MCUOX). The comparison shows that the proposed GA-Fuzzy technique outperforms both the GA-PMX and GA-MCUOX.

Suggested Citation

  • K. Raja & C. Arumugam & V. Selladurai, 2008. "Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 4(1), pages 72-101.
    • Handle: RePEc:ids:ijsoma:v:4:y:2008:i:1:p:72-101
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    Citations

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    Cited by:

    1. Jürgen Strohhecker & Michael Hamann & Jörn-Henrik Thun, 2016. "Loading and sequencing heuristics for job scheduling on two unrelated parallel machines with long, sequence-dependent set-up times," International Journal of Production Research, Taylor & Francis Journals, vol. 54(22), pages 6747-6767, November.
    2. Chang, Zhiqi & Ding, Jian-Ya & Song, Shiji, 2019. "Distributionally robust scheduling on parallel machines under moment uncertainty," European Journal of Operational Research, Elsevier, vol. 272(3), pages 832-846.

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