Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2020

Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation

  • Dades identificatives

    Identificador: RP:4905
    Handle: https://hdl.handle.net/20.500.11797/RP4905
    Autors:
    Kelton, W. DavidCordón, OscarBayliss, ChristopherJuan, Angel A.Chica, Manuel
    Resum:
    Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These ‘simheuristic’ algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a ‘first-resort’ method when addressing large-scale and NP-hard optimization problems under uncertainty –which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages.

  • Altres:

    Autor segons l'article: Kelton, W. David Cordón, Oscar Bayliss, Christopher Juan, Angel A. Chica, Manuel
    Paraules clau: Simulation
    Resum: Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These ‘simheuristic’ algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a ‘first-resort’ method when addressing large-scale and NP-hard optimization problems under uncertainty –which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages.
    Any de publicació de la revista: 2020
    Tipus de publicació: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article