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Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system

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  • Diaz, Juan Esteban
  • Handl, Julia
  • Xu, Dong-Ling

Abstract

This paper considers a real-world production planning problem in which production line failures cause uncertainty regarding the practical implementation of a given production plan. We provide a general formulation of this problem as an extended stochastic knapsack problem, in which uncertainty arises from non-trivial perturbations to the decision variables that cannot be represented in closed form.

Suggested Citation

  • Diaz, Juan Esteban & Handl, Julia & Xu, Dong-Ling, 2018. "Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system," European Journal of Operational Research, Elsevier, vol. 266(3), pages 976-989.
    • Handle: RePEc:eee:ejores:v:266:y:2018:i:3:p:976-989
    DOI: 10.1016/j.ejor.2017.10.062
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    Cited by:

    1. Diaz, Juan Esteban & López-Ibáñez, Manuel, 2021. "Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1209-1222.
    2. Homsi, Gabriel & Jordan, Jeremy & Martello, Silvano & Monaci, Michele, 2021. "The assignment and loading transportation problem," European Journal of Operational Research, Elsevier, vol. 289(3), pages 999-1007.
    3. Martello, Silvano & Monaci, Michele, 2020. "Algorithmic approaches to the multiple knapsack assignment problem," Omega, Elsevier, vol. 90(C).
    4. Stefka Fidanova & Krassimir Todorov Atanassov, 2021. "ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem," Mathematics, MDPI, vol. 9(13), pages 1-7, June.

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