Distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times using a novel biogeography-based optimization algorithm
Jialin Huang, Xingsheng Gu
Abstract
This article proposes a novel biogeography-based optimization (NBBO) algorithm to solve the distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times (DAPFSP-SDST). The optimization objective of this problem is minimizing the maximum completion time (makespan). In the initialization phase, NBBO generates two kinds of feasible solutions. Secondly, the linear migration model is replaced with the sinusoidal migration model and a modified product insertion method is performed in the migration phase. Then, in the mutation phase, a job insertion method is used to adjust the processing order of jobs in each product. A local search method based on SDST is combined to jump out of local optima. Finally, simulation experiments based on 540 test instances and comparisons with seven existing algorithms as well as one simple biogeography-based optimization algorithm verify the superiority of NBBO.