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Metaheuristic Approaches for Scheduling Jobs on Parallel Batch Processing Machines

In: Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling

Author

Listed:
  • Stefan Lausch

    (University of Hagen)

  • Lars Mönch

    (University of Hagen)

Abstract

We consider a scheduling problem for parallel identical batch processing machines. A batch is a set of jobs that can be processed at the same time on a single machine. The jobs belong to incompatible job families. Only jobs of the same family can be batched together. We are interested in minimizing the total weighted tardiness (TWT) of the jobs. Problems of this type arise, for instance, in semiconductor manufacturing. Other known occurrence of batch processing machines can be found in gear manufacturing. We describe a genetic algorithm (GA), an ant colony optimization (ACO) approach, and a large neighborhood search (LNS) approach for this scheduling problem. The performance of the three metaheuristic approaches is compared based on randomly generated problem instances. The LNS scheme outperforms the two other metaheuristics and is comparable with a variable neighborhood search (VNS) approach, the best performing heuristic for this scheduling problem from the literature.

Suggested Citation

  • Stefan Lausch & Lars Mönch, 2016. "Metaheuristic Approaches for Scheduling Jobs on Parallel Batch Processing Machines," International Series in Operations Research & Management Science, in: Ghaith Rabadi (ed.), Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edition 1, chapter 0, pages 187-207, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-26024-2_10
    DOI: 10.1007/978-3-319-26024-2_10
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    Cited by:

    1. 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.

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