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A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system

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  • Xu, Rui
  • Chen, Huaping
  • Li, Xueping

Abstract

This research aims to minimize the bi-criteria of makespan and maximum tardiness on a set of identical batch-processing machines arranged in parallel. Each machine can process multiple jobs simultaneously as long as the machine capacity is not exceeded. Each job is defined by its processing time, ready time, due date, and size. The processing time and ready time of a batch are represented by the largest processing time and release time among all jobs in the batch, respectively. For this problem, a scheduling algorithm based on the framework of a multi-objective ant colony optimization (MOACO) approach called a Pareto-based ant colony system (PACS) was developed. Based on the constructive characteristics of PACS, a new mechanism of solution construction was introduced so that the proposed algorithm had the ability to explore the entire solution space. Moreover, corresponding to the new construction mechanism, a candidate list strategy and a form of dynamic heuristic information were developed to reduce the search space and direct the search toward the promising regions, respectively. Through extensive computational experiments with various problem instances, the effectiveness of the proposed algorithm was evaluated by measuring the computational efficiency and solution quality. The experiment results demonstrated that PACS had a superior performance compared to other benchmark algorithms, especially for large job instances.

Suggested Citation

  • Xu, Rui & Chen, Huaping & Li, Xueping, 2013. "A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system," International Journal of Production Economics, Elsevier, vol. 145(1), pages 371-386.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:371-386
    DOI: 10.1016/j.ijpe.2013.04.053
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    References listed on IDEAS

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    1. Damodaran, Purushothaman & Kumar Manjeshwar, Praveen & Srihari, Krishnaswami, 2006. "Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms," International Journal of Production Economics, Elsevier, vol. 103(2), pages 882-891, October.
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    Cited by:

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    2. Jia, Zhao-hong & Leung, Joseph Y.-T., 2015. "A meta-heuristic to minimize makespan for parallel batch machines with arbitrary job sizes," European Journal of Operational Research, Elsevier, vol. 240(3), pages 649-665.
    3. XiaoLin Li & YuPeng Li & Yu Wang, 2017. "Minimising makespan on a batch processing machine using heuristics improved by an enumeration scheme," International Journal of Production Research, Taylor & Francis Journals, vol. 55(1), pages 176-186, January.
    4. Jia, Zhao-hong & Li, Kai & Leung, Joseph Y.-T., 2015. "Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities," International Journal of Production Economics, Elsevier, vol. 169(C), pages 1-10.
    5. Gilseung Ahn & Sun Hur, 2020. "Clustering and Dispatching Rule Selection Framework for Batch Scheduling," Mathematics, MDPI, vol. 8(1), pages 1-14, January.
    6. Liang Tang & Zhihong Jin & Xuwei Qin & Ke Jing, 2019. "Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design," Annals of Operations Research, Springer, vol. 275(2), pages 685-714, April.
    7. 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.
    8. Onur Ozturk, 2020. "A bi-criteria optimization model for medical device sterilization," Annals of Operations Research, Springer, vol. 293(2), pages 809-831, October.

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