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An optimization-based heuristic for the robotic cell problem

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  • Carlier, Jacques
  • Haouari, Mohamed
  • Kharbeche, Mohamed
  • Moukrim, Aziz

Abstract

This study investigates an optimization-based heuristic for the robotic cell problem. This problem arises in automated cells and is a complex flow shop problem with a single transportation robot and a blocking constraint. We propose an approximate decomposition algorithm. The proposed approach breaks the problem into two scheduling problems that are solved sequentially: a flow shop problem with additional constraints (blocking and transportation times) and a single machine problem with precedence constraints, time lags, and setup times. For each of these problems, we propose an exact branch-and-bound algorithm. Also, we describe a genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that the proposed optimization-based approach delivers high-quality solutions and consistently outperforms the genetic algorithm. However, the genetic algorithm delivers reasonably good solutions while requiring significantly shorter CPU times.

Suggested Citation

  • Carlier, Jacques & Haouari, Mohamed & Kharbeche, Mohamed & Moukrim, Aziz, 2010. "An optimization-based heuristic for the robotic cell problem," European Journal of Operational Research, Elsevier, vol. 202(3), pages 636-645, May.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:3:p:636-645
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    References listed on IDEAS

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    Cited by:

    1. Christophe Sauvey & Wajdi Trabelsi & Nathalie Sauer, 2020. "Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
    2. Mohammad Reza Komari Alaei & Mehmet Soysal & Atabak Elmi & Audrius Banaitis & Nerija Banaitiene & Reza Rostamzadeh & Shima Javanmard, 2021. "A Bender’s Algorithm of Decomposition Used for the Parallel Machine Problem of Robotic Cell," Mathematics, MDPI, vol. 9(15), pages 1-15, July.
    3. Sobhani, A. & Wahab, M.I.M. & Neumann, W.P., 2017. "Incorporating human factors-related performance variation in optimizing a serial system," European Journal of Operational Research, Elsevier, vol. 257(1), pages 69-83.
    4. Xin Li & Richard Y. K. Fung, 2016. "Optimal K-unit cycle scheduling of two-cluster tools with residency constraints and general robot moving times," Journal of Scheduling, Springer, vol. 19(2), pages 165-176, April.
    5. Mehdi Mrad & Anis Gharbi & Mohamed Haouari & Mohamed Kharbeche, 2016. "An optimization-based heuristic for the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 115-132, July.
    6. Fatemi-Anaraki, Soroush & Tavakkoli-Moghaddam, Reza & Foumani, Mehdi & Vahedi-Nouri, Behdin, 2023. "Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear Programming and Constraint Programming Approaches," Omega, Elsevier, vol. 115(C).

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