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Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics

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  • Mosadegh, H.
  • Fatemi Ghomi, S.M.T.
  • Süer, G.A.

Abstract

This paper presents a mixed-model sequencing problem with stochastic processing times (MMSPSP) in a multi-station assembly line. A new mixed-integer nonlinear programing model is developed to minimize weighted sum of expected total work-overload and idleness, which is converted into a mixed-integer linear programming model to deal with small-sized instances optimally. Due to the NP-hardness of the problem, this paper develops a novel hyper simulated annealing (HSA). The HSA employs a Q-learning algorithm to select appropriate heuristics through its search process. Numerical results are presented on several test instances and benchmark problems from the related literature. The results of statistical analysis indicate that the HSA is quite competitive in comparison with optimization software packages, and is significantly superior to several SA-based algorithms. The results highlight the advantages of the MMSPSP in comparison with traditional deterministic approaches in mixed-model sequencing contexts.

Suggested Citation

  • Mosadegh, H. & Fatemi Ghomi, S.M.T. & Süer, G.A., 2020. "Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 282(2), pages 530-544.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:530-544
    DOI: 10.1016/j.ejor.2019.09.021
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    1. Pâmela M.C. Cortez & Alysson M. Costa, 2015. "Sequencing mixed-model assembly lines operating with a heterogeneous workforce," International Journal of Production Research, Taylor & Francis Journals, vol. 53(11), pages 3419-3432, June.
    2. Prandtstetter, Matthias & Raidl, Günther R., 2008. "An integer linear programming approach and a hybrid variable neighborhood search for the car sequencing problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1004-1022, December.
    3. Golle, Uli & Rothlauf, Franz & Boysen, Nils, 2014. "Car sequencing versus mixed-model sequencing: A computational study," European Journal of Operational Research, Elsevier, vol. 237(1), pages 50-61.
    4. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2009. "Sequencing mixed-model assembly lines: Survey, classification and model critique," European Journal of Operational Research, Elsevier, vol. 192(2), pages 349-373, January.
    5. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    6. Delorme, Xavier & Dolgui, Alexandre & Kovalev, Sergey & Kovalyov, Mikhail Y., 2019. "Minimizing the number of workers in a paced mixed-model assembly line," European Journal of Operational Research, Elsevier, vol. 272(1), pages 188-194.
    7. Bautista, Joaquín & Cano, Alberto, 2011. "Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimisation and interruption rules," European Journal of Operational Research, Elsevier, vol. 210(3), pages 495-513, May.
    8. Mansouri, S. Afshin, 2005. "A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines," European Journal of Operational Research, Elsevier, vol. 167(3), pages 696-716, December.
    9. Xiaobo Zhao & Jianyong Liu & Katsuhisa Ohno & Shigenori Kotani, 2007. "Modeling and analysis of a mixed‐model assembly line with stochastic operation times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(6), pages 681-691, September.
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