Intelligent scheduling in flexible job shop environments based on artificial fish swarm algorithm with estimation of distribution

Abstract

Efficient scheduling strategy is crucial to a manufacturing system in flexible job shop environments. The flexible job shop scheduling problem (FJSP) is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation sequence. In this paper, an efficient artificial fish swarm model with estimation of distribution (AFSA-ED) is proposed for obtaining intelligent scheduling strategies. In AFSA-ED, an integrated initialization algorithm is proposed for machine assignment and operation sequence initialization, and then the population is divided into two sub-populations and evolved respectively. Moreover, the designed pre-principle and post-principle arranging mechanism are applied to the different sub-populations for enhancing the diversity. Following this, an artificial fish swarm algorithm with estimation of distribution is proposed to explore the search space for promising solutions. Besides, an attracting behavior is designed to improve the global exploration ability and a public factor based critical path search strategy is presented to enhance the local exploitation ability. Simulated experiments are carried on BRdata, BCdata and HUdata benchmark sets. The statistical computation results validate the performance of the proposed algorithm in solving the FJSP, as compared with some other state of the art algorithms.