An estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the multi-objective flexible job-shop scheduling problem (MFJSP) to minimize the maximum completion time, the total workload of machines and the workload of the critical machine simultaneously. Within the framework of EDA, the new individuals are generated by sampling a probability model, which is built and updated with the superior sub-population by a proposed mechanism. Moreover, the EDA utilizes multiple strategies in a combination way to generate the initial solutions, and used a local search strategy based on critical path to enhance the exploitation ability. Based on the Taguchi method of design-of-experiment, the influence of parameter setting is investigated and suitable parameters are suggested. Finally, numerical simulation based on some well-known benchmarks and comparisons with some existing algorithms are carried out. The results demonstrate the effectiveness of the proposed EDA to solve the MFJSP.