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Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem with Lot-Streaming of Random Breakdown

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13164))

Abstract

An improved migrating birds optimization (IMBO) algorithm is proposed to solve the hybrid flowshop scheduling problem with lot-streaming of random breakdown (RBHLFS) with the aim of minimizing the total flow time. To ensure the diversity of the initial population, a Nawaz-Enscore-Ham (NEH) heuristic algorithm is used. A greedy algorithm is used to construct a combined neighborhood search structure. An effective local search procedure is utilized to explore potential promising neighborhoods. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparisons demonstrate the feasibility and effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

  1. University of Camerino, Camerino, MC, 62022, Italy

    Ping Wang & Renato De Leone

  2. Liaocheng University, Liaocheng, LC, 252000, China

    Ping Wang & Hongyan Sang

Authors
  1. Ping Wang
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  2. Renato De Leone
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  3. Hongyan Sang
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Corresponding author

Correspondence to Renato De Leone .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Wang, P., De Leone, R., Sang, H. (2022). Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem with Lot-Streaming of Random Breakdown. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13164. Springer, Cham. https://doi.org/10.1007/978-3-030-95470-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-95470-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95469-7

  • Online ISBN: 978-3-030-95470-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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