Improved EDA-Based Hyper-heuristic for Flexible Job Shop Scheduling Problem with Sequence-Independent Setup Times and Resource Constraints.

In this paper, an improved estimation of distribution algorithm-based hyper heuristic (IEDA-HH) algorithm is proposed for the flexible job shop scheduling problem with sequence-independent setup times and resource constraints (FJSP_SISTs_RCs) with the objective of minimizing the makespan. First, a single-vector encoding scheme is employed to represent the ranking of solutions, and a LPT (Longest Processing Time) based method is designed to select the machine to process jobs. Second, six simple and efficient heuristics are incorporated into IEDA-HH to constitute a set of low-level heuristics (LLHs). Third, the improved estimation of distribution algorithm (IEDA) is introduced as the high-level strategy to manage the heuristic sequences from the pre-designed LLHs set. In each generation, the heuristic sequences are evolved from the IEDA and then sequentially performed on the solution space for better results. Finally, the performance of IEDA-HH is carried out on a typical benchmark dataset and computational results demonstrate the superiority of the proposed IEDA-HH.