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A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty

Author

Listed:
  • Juliana Castaneda

    (Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Xabier A. Martin

    (Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Majsa Ammouriova

    (Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Javier Panadero

    (Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Angel A. Juan

    (Department of Applied Statistics and Operations Research, Universitat Politècnica de València, 03801 Alcoy, Spain)

Abstract

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with both stochastic and fuzzy processing times. The main goal is to find the solution (permutation of jobs) that minimizes the expected makespan. However, due to the existence of uncertainty, other characteristics of the solution are also taken into account. In particular, we illustrate how survival analysis can be employed to enrich the probabilistic information given to decision-makers. To solve the aforementioned optimization problem, we extend the concept of a simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated in the PFSP. In order to test our approach, classical PFSP instances have been adapted and extended, so that processing times become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones.

Suggested Citation

  • Juliana Castaneda & Xabier A. Martin & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2022. "A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty," Mathematics, MDPI, vol. 10(10), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1760-:d:820702
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    References listed on IDEAS

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