Advances in Swarm Intelligence

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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