An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling

The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial bee colony (ABC) with self-adaptive operators and alterable search depth (SOAD) approach is presented in this paper. By introducing SOAD into the employed bee phase, the adoption of genetic operators and the corresponding search depth are adaptively determined according to their historical performance. With respect to the objective of minimizing total weighted tardiness, the proposed approach is compared with the others self-adaptive strategy for selecting operators and population-based metaheuristics. Computational results show that the proposed approach can maintain a better balance between global exploration and local exploitation.