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A Population-Based Framework for Solving the Job Shop Scheduling Problem

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

The paper proposes the framework named MPF, extending the Mushroom Picking Metaheuristics and originally proposed earlier by the authors. The framework can be used for solving combinatorial optimization problems. In the current study, the framework has been used for solving instances of the Job Shop Scheduling Problem (JSSP). The framework allows defining several solutions improving agents. Agents work in parallel trying to improve solutions. Solutions are maintained on two levels – common memory and sub-populations for each thread. The framework provides functionality allowing the implementation of a strategy for maintenance of threads and the common memory, including the information exchange between them. For the JSSP implementation, we propose 5 types of autonomous agents. The computational experiment carried out using benchmark datasets has confirmed the good performance of the proposed approach in terms of solutions quality and computation times.

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Authors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Piotr Jedrzejowicz, Ewa Ratajczak-Ropel & Izabela Wierzbowska

Authors
  1. Piotr Jedrzejowicz
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  2. Ewa Ratajczak-Ropel
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  3. Izabela Wierzbowska
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Corresponding author

Correspondence to Izabela Wierzbowska .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Jedrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I. (2021). A Population-Based Framework for Solving the Job Shop Scheduling Problem. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

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