Abstract
Because of the importance of the permutation flowshop scheduling problem, a variety of researches are being focused on this issue and numerous algorithms have been proposed. However, many uncertainties may exist in real production environment of the permutation flowshop problem. In order to solve this obstacle, an optimization criterion of maximizing the probability of ensuring the makespan not surpass the expected finish time is considered for M-machine permutation flowshop in this chapter. A schedule for this optimization criterion is called the robust schedule. A novel artificial bee colony (ABC) algorithm integrated with an efficient local search is proposed. The local search introduces into a probability model to determine whether the new generated solution should be accepted. For experiment analysis, the performance of proposed ABC is evaluated on the well-known Car and Rec permutation flowshop problems which are taken from OR library and is compared with an improved genetic algorithm and NEH heuristic. The comparison results indicate that the proposed ABC performs well and can give better robust schedules for M-machine permutation flowshop problem.
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Ma, S., Wang, Y., Li, M. (2019). A Novel Artificial Bee Colony Algorithm for Robust Permutation Flowshop Scheduling. In: Li, X., Wong, KC. (eds) Natural Computing for Unsupervised Learning. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-98566-4_8
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DOI: https://doi.org/10.1007/978-3-319-98566-4_8
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