Abstract—In this paper, the product sequencing decisions in multiple-piece-flow assembly lines problem is approximately solved with a hybrid evolutionary algorithm. The product sequencing decisions in multiple-piece-flow assembly lines, known as the sequencing m-vector bin packing problem, occurs in manufacturing organization and because of its NP-hardness it is however computationally challenging. The designed method combines a population approach and both first fit bin packing procedure coupled with a repairing operator: the population approach tries to maintain the diversity of a series of populations reached throughout an iterative procedure while the added operators try to highlight the quality of the solutions throughout the search process. The performance of the proposed method is evaluated on a set of benchmark instances taken from the literature. The results provided by the method are compared to those reached by recent published methods and to those reached by the state-of-the-art Cplex solver. The preliminary experimental part showed that the designed method outperforms the other ones by discovering new bounds for most of considered instances.
 

Index Terms—evolutionary, intensification, optimization, packing, sequencing

Cite: Méziane Aïder, Amina N. Benahmed, Isma Dahmani, and Mhand Hifi, "A Hybrid Evolutionary Algorithm for the Sequencing m-Vector Bin Packing Problem," Journal of Advances in Information Technology, Vol. 13, No. 4, pp. 306-311, August 2022.

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