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Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms

Author

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  • Purushothaman Damodaran
  • Neal S. Hirani
  • Mario C. Velez-Gallego

Abstract

This paper aims to minimise the makespan of a set of identical batch processing machines in parallel. The batch processing machine can process a batch of jobs as long as the total size of all the jobs in the batch does not exceed its capacity. The processing time of the job and its size are given. Batch processing time is equal to the longest processing job in the batch. Two interdependent decisions are required, namely grouping jobs into batches, and scheduling the batches on the machines. The problem under study is NP-hard and hence a Genetic Algorithm (GA) approach is proposed. The effectiveness of the GA approach to solve randomly generated problems was compared with a Simulated Annealing (SA) approach, a Random Keys Genetic Algorithm (RKGA), a Hybrid Genetic Heuristic (HGH) and a commercial solver. The proposed GA approach was found to be very effective in finding a good solution in a short time as opposed to SA, RKGA and a commercial solver. Both GA and HGH are marginally better than each other on different problem instances. [Submitted 17 August 2007; Revised 10 October 2007; Revised 30 May 2008; Revised 29 July 2008; Accepted 15 September 2008]

Suggested Citation

  • Purushothaman Damodaran & Neal S. Hirani & Mario C. Velez-Gallego, 2009. "Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 187-206.
    • Handle: RePEc:ids:eujine:v:3:y:2009:i:2:p:187-206
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    Citations

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    Cited by:

    1. Paz Perez-Gonzalez & Jose M. Framinan, 2018. "Single machine interfering jobs problem with flowtime objective," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 953-972, June.
    2. Hadi Mokhtari & Amir Noroozi, 2018. "An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1063-1081, June.
    3. Vallada, Eva & Ruiz, Rubén, 2011. "A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 211(3), pages 612-622, June.
    4. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    5. Zhou, Shengchao & Liu, Ming & Chen, Huaping & Li, Xueping, 2016. "An effective discrete differential evolution algorithm for scheduling uniform parallel batch processing machines with non-identical capacities and arbitrary job sizes," International Journal of Production Economics, Elsevier, vol. 179(C), pages 1-11.
    6. Mojtaba Afzalirad & Masoud Shafipour, 2018. "Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 423-437, February.
    7. Muter, İbrahim, 2020. "Exact algorithms to minimize makespan on single and parallel batch processing machines," European Journal of Operational Research, Elsevier, vol. 285(2), pages 470-483.

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