Abstract
This paper investigates a highly relevant real world scheduling problem, namely the multi-objective flexible job shop scheduling problem (FJSP) with sequence-dependent set-up times, auxiliary resources and machine down time. A hyper-heuristic algorithm is presented which makes use of a set of meta-heuristic algorithms which are self-adaptively selected at different stages of the optimization process to optimize a set of candidate solutions. This meta-hyper-heuristic algorithm was tested on a number of real world production scheduling data sets and was also benchmarked against the previous state-of-the-art job shop scheduling algorithms applied to this specific problem. In addition to the competitive results obtained, the self-adaptive nature of the algorithm avoids the resource intensive process of developing a meta-heuristic algorithm for one specific problem instance.
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Grobler, J., Engelbrecht, A.P. (2016). Hyper-heuristics for the Flexible Job Shop Scheduling Problem with Additional Constraints. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_1
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DOI: https://doi.org/10.1007/978-3-319-41009-8_1
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