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Hybrid Metaheuristic Approaches for Makespan Minimization on a Batch Processing Machine

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Metaheuristics (MIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13838))

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Abstract

A batch processing machine (BPM) is characterized as being able to process multiple jobs simultaneously. This type of machines is common in industrial processes such as electrolytic coating, heat treatments and drying ovens. The BPM scheduling problem consists of grouping a set of jobs into batches to be processed in a single machine with a limited capacity, in such a way that the time necessary to manufacture all jobs (makespan) is minimized. The BPM scheduling problem can be formulated as a mixed integer linear program (MILP). Nevertheless, it is usually addressed through metaheuristic algorithms due to it belongs to NP-Hard class of problems. In this paper, techniques such as the savings methods, NEH algorithm, Large Neighborhood Search (LNS) metaheuristic and splitting algorithm (order-first cluster-second) are adapted to solve the BPM. The performance of the algorithms is evaluated using known instances from literature with up to 100 jobs. The proposed algorithms improve some of the best known solutions in the literature.

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Authors and Affiliations

  1. Mathematical Modeling Research Group, Universidad EAFIT, Carrera 49 # 7 sur - 50, Medellín, Colombia

    Juan Carlos Rivera & Ana María Cortes

Authors
  1. Juan Carlos Rivera
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  2. Ana María Cortes
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Correspondence to Juan Carlos Rivera .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Luca Di Gaspero

  2. University of Napoli - Federico II, Napoli, Italy

    Paola Festa

  3. University of Paris Est, Paris, France

    Amir Nakib

  4. University of Catania, Catania, Italy

    Mario Pavone

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Rivera, J.C., Cortes, A.M. (2023). Hybrid Metaheuristic Approaches for Makespan Minimization on a Batch Processing Machine. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_43

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  • DOI: https://doi.org/10.1007/978-3-031-26504-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26503-7

  • Online ISBN: 978-3-031-26504-4

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