A hybrid differential evolution for general multi-objective flow shop problem with a modified learning effect

Homa Amirian | Rashed Sahraeian

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=42841
Date :  2016/10/11
Publish in :    Proceedings of the Institution of Mechanical Engineers - Part B: Journal of Engineering Manufacture
DOI :  https://doi.org/10.1177/0954405416673094

Keywords :differential, evolution, general, learning

Abstract :

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In this article, a modification of multi-objective differential evolution based on simulated annealing is proposed to solve a general tri-objective non-permutation flow shop problem. The flow shop system considers the release dates, machine breakdowns, past-sequence-dependent setup times and learning effect for all the jobs. The algorithm proposed to tackle such a model combines the robustness of differential evolution with the rapid convergence and conditional diversification of simulated annealing. For small and medium low-sized problems, the solutions found by the proposed algorithm are compared with the exact solutions, achieved by augmented e-constraint method. Due to the high complexity of the model, for medium high and large-sized problems, the algorithm is tested against the imperialist competitive algorithm and the multi-objective differential evolution scheduling. Comparisons of the results show a good balance between intensification and diversification in the proposed algorithm.