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A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer

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  • Almeder, Christian
  • Hartl, Richard F.
Abstract
This work deals with a scheduling problem of a real-world production process in the metal–working industry. The production process can be described as an offline stochastic flexible flow-shop problem with limited buffers. In a first step, we analyze a simplified model and develop a variable neighborhood search based solution approach where we use multiple scenarios to evaluate the objective. Second, the solution approach is adapted to a real-world case using a detailed discrete-event simulation to evaluate the production plans. We are able to improve state-of-the-art production plans statistically significant by 3–10%.

Suggested Citation

  • Almeder, Christian & Hartl, Richard F., 2013. "A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer," International Journal of Production Economics, Elsevier, vol. 145(1), pages 88-95.
  • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:88-95
    DOI: 10.1016/j.ijpe.2012.09.014
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    References listed on IDEAS

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    2. Hansen, Pierre & Mladenovic, Nenad & Moreno Pérez, Jos´e A., 2008. "Variable neighborhood search," European Journal of Operational Research, Elsevier, vol. 191(3), pages 593-595, December.
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    4. Kis, Tamas & Pesch, Erwin, 2005. "A review of exact solution methods for the non-preemptive multiprocessor flowshop problem," European Journal of Operational Research, Elsevier, vol. 164(3), pages 592-608, August.
    5. Hemmelmayr, Vera C. & Doerner, Karl F. & Hartl, Richard F., 2009. "A variable neighborhood search heuristic for periodic routing problems," European Journal of Operational Research, Elsevier, vol. 195(3), pages 791-802, June.
    6. Russell W. Bent & Pascal Van Hentenryck, 2004. "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers," Operations Research, INFORMS, vol. 52(6), pages 977-987, December.
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    Cited by:

    1. 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.
    2. Li, Zhan-tao & Chen, Qing-xin & Mao, Ning & Wang, Xiaoming & Liu, Jianjun, 2013. "Scheduling rules for two-stage flexible flow shop scheduling problem subject to tail group constraint," International Journal of Production Economics, Elsevier, vol. 146(2), pages 667-678.
    3. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    4. Anzhen Peng & Longcheng Liu & Weifeng Lin, 2021. "Improved approximation algorithms for two-stage flexible flow shop scheduling," Journal of Combinatorial Optimization, Springer, vol. 41(1), pages 28-42, January.
    5. Gerstl, Enrique & Mosheiov, Gur, 2014. "A two-stage flexible flow shop problem with unit-execution-time jobs and batching," International Journal of Production Economics, Elsevier, vol. 158(C), pages 171-178.
    6. Minghui Zhang & Yan Lan & Xin Han, 2020. "Approximation algorithms for two-stage flexible flow shop scheduling," Journal of Combinatorial Optimization, Springer, vol. 39(1), pages 1-14, January.

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