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Heuristic approaches for mixed-model sequencing problem with stochastic processing times

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

Abstract

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.

Suggested Citation

  • H. Mosadegh & S.M.T. Fatemi Ghomi & G.A. Süer, 2017. "Heuristic approaches for mixed-model sequencing problem with stochastic processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2857-2880, May.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:10:p:2857-2880
    DOI: 10.1080/00207543.2016.1223897
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    Cited by:

    1. Janis Brammer & Bernhard Lutz & Dirk Neumann, 2022. "Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 29-56, March.
    2. Masoud Rabbani & Mahdi Mokhtarzadeh & Neda Manavizadeh & Azadeh Farsi, 2021. "Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance," OPSEARCH, Springer;Operational Research Society of India, vol. 58(3), pages 513-539, September.
    3. Asieh Varyani & Mohsen Salehi & Meysam Heydari Gharahcheshmeh, 2024. "Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing," Energies, MDPI, vol. 17(12), pages 1-20, June.

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