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Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios

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Listed:
  • Diego Oliva

    (IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara 44160, Mexico)

  • Pedro Copado

    (IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Salvador Hinojosa

    (Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara 44160, Mexico
    Tecnologico de Monterrey, School of Engineering and Science, Zapopan 45201, Mexico)

  • Javier Panadero

    (IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Daniel Riera

    (IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Angel A. Juan

    (IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

Abstract

Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements.

Suggested Citation

  • Diego Oliva & Pedro Copado & Salvador Hinojosa & Javier Panadero & Daniel Riera & Angel A. Juan, 2020. "Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2240-:d:464393
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    References listed on IDEAS

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    1. Ghodsypour, S. H. & O'Brien, C., 1998. "A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming," International Journal of Production Economics, Elsevier, vol. 56(1), pages 199-212, September.
    2. Chao, I-Ming & Golden, Bruce L. & Wasil, Edward A., 1996. "The team orienteering problem," European Journal of Operational Research, Elsevier, vol. 88(3), pages 464-474, February.
    3. Faulin, Javier & Juan, Angel A. & Serrat, Carles & Bargueño, Vicente, 2008. "Predicting availability functions in time-dependent complex systems with SAEDES simulation algorithms," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1761-1771.
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    Cited by:

    1. Omer Ozkan & Sezgin Kilic, 2023. "UAV routing by simulation-based optimization approaches for forest fire risk mitigation," Annals of Operations Research, Springer, vol. 320(2), pages 937-973, January.
    2. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.
    3. 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.

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