A Novel Evolutionary Algorithm with Adaptation Mechanism for Fuzzy Permutation Flow-Shop Scheduling

As a scheduling problem with wide application backgrounds, the flow-shop scheduling problem (FSP) in deterministic cases has attracted much attention. However, the neglecting of uncertainty will greatly diminish the application value of scheduling results, which makes it necessary to incorporate the uncertainty in the FSP. In this paper, a fuzzy permutation flow-shop scheduling problem (FPFSP) is considered and a novel evolutionary algorithm with adaptation mechanism (AMEA) is proposed to minimize fuzzy makespan. In the initialization phase, two initialization strategies based on the NEH heuristic are proposed to improve the quality of initial population. In the evolution phase, to enhance the exploitation, multiple local search operators are conducted in a collaborative way where the utilizations of operators are adjusted adaptively according to the feedback of their performances; besides, to save computing resources and balance the exploration and exploitation, the population size is adjusted adaptively with the number of generations. Benchmark instances are generated to evaluate the performance of the AMEA. The experimental results and statistical comparisons show that the proposed algorithm has great advantages in solving the FPFSP.