An effective fruit fly optimization algorithm for the distributed permutation flowshop scheduling problem with total flowtime

Distributed permutation flowshop scheduling problem (DPFSP) has always been a hot issue. The optimization goal of minimizing the total flowtime is of great significance to the environment of multi-factory. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the DPFSP with the total flowtime criterion. In the proposed DFFO, an initialization method considering the population quality and diversity is adopted. In the smell-based search stage, three perturbation operators, the Shift-based operator, the Exchange-based operator and the Hybrid operator are designed respectively, and each fruit fly improves its state through a specific neighborhood strategy. In addition, we propose an improved reference local search (MRLS) method to enhance the exploitation ability of fruit flies. In the vision-based search stage, fruit flies use a well-designed combination update mechanism to lead fruit flies to more potential areas. In order to enhance the exploration ability, we use random reinforcement method for the population. The parameters are evaluated using an orthogonal experimental design to determine the appropriate values of the key parameters. In addition, we test the DFFO and the state-of-art algorithms from the literature on 720 large-scale instances. The experimental results show that DFFO is a very effective metaheuristic.

Automated summary

In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) with the goal of minimizing total flowtime. The DFFO algorithm adopts an initialization method that considers population quality and diversity, and it includes three perturbation operators and an improved reference local search method to improve its exploration and exploitation abilities. Experimental results on large-scale instances demonstrate the effectiveness of DFFO as a metaheuristic algorithm.