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

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

International Journal of Production Research, 2017, vol. 55, issue 10, 2857-2880

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.

Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1080/00207543.2016.1223897

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