A genetic biogeography–based optimization algorithm for the distributed assembly permutation flow–shop scheduling problem

In recent years, the distributed assembly permutation flow-shop scheduling problem (DAPFSP) has been studied frequently. Due to the high complexity of DAPFSP, the results of the benchmark instances are not particularly desirable. In order to solve the disadvantages of meta-heuristics in DAPFSP, such as being easy to fall into local optima and poor stability, a genetic biogeography-based optimization (GBBO) algorithm is proposed to address DAPFSP with minimize makespan. A DAPFSP mathematical model is established. The crossover operator of genetic algorithm is integrated into the migration operator to improve the quality of the emigration solution. The mobility model is modified to improve the searching ability of the algorithm, and the method of product sequence fusion (PSF) is designed for deep searching of partial solutions. Through simulation experiments, GBBO refreshed 155 benchmark instances in both small-sized and large-sized instances.