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Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

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  • Mehravaran, Yasaman
  • Logendran, Rasaratnam

Abstract

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.

Suggested Citation

  • Mehravaran, Yasaman & Logendran, Rasaratnam, 2012. "Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times," International Journal of Production Economics, Elsevier, vol. 135(2), pages 953-963.
    • Handle: RePEc:eee:proeco:v:135:y:2012:i:2:p:953-963
    DOI: 10.1016/j.ijpe.2011.11.011
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    References listed on IDEAS

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    1. Moslehi, G. & Mirzaee, M. & Vasei, M. & Modarres, M. & Azaron, A., 2009. "Two-machine flow shop scheduling to minimize the sum of maximum earliness and tardiness," International Journal of Production Economics, Elsevier, vol. 122(2), pages 763-773, December.
    2. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    3. Onwubolu, Godfrey & Davendra, Donald, 2006. "Scheduling flow shops using differential evolution algorithm," European Journal of Operational Research, Elsevier, vol. 171(2), pages 674-692, June.
    4. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    5. Aggoune, Riad & Portmann, Marie-Claude, 2006. "Flow shop scheduling problem with limited machine availability: A heuristic approach," International Journal of Production Economics, Elsevier, vol. 99(1-2), pages 4-15, February.
    6. Koksalan, Murat & Burak Keha, Ahmet, 2003. "Using genetic algorithms for single-machine bicriteria scheduling problems," European Journal of Operational Research, Elsevier, vol. 145(3), pages 543-556, March.
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    Cited by:

    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    3. Dolgui, Alexandre & Kovalev, Sergey & Kovalyov, Mikhail Y. & Nossack, Jenny & Pesch, Erwin, 2014. "Minimizing setup costs in a transfer line design problem with sequential operation processing," International Journal of Production Economics, Elsevier, vol. 151(C), pages 186-194.
    4. M. Henneberg & J.S. Neufeld, 2016. "A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3534-3550, June.
    5. Jun Pei & Xinbao Liu & Panos M. Pardalos & Wenjuan Fan & Ling Wang & Shanlin Yang, 2016. "Solving a supply chain scheduling problem with non-identical job sizes and release times by applying a novel effective heuristic algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(4), pages 765-776, March.
    6. E. Dhouib & J. Teghem & T. Loukil, 2018. "Non-permutation flowshop scheduling problem with minimal and maximal time lags: theoretical study and heuristic," Annals of Operations Research, Springer, vol. 267(1), pages 101-134, August.
    7. Xiao, Yiyong & Yuan, Yingying & Zhang, Ren-Qian & Konak, Abdullah, 2015. "Non-permutation flow shop scheduling with order acceptance and weighted tardiness," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 312-333.

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