Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming
Wang Ping, Hongyan Sang, Qiuyun Tao, Hengwei Guo, Junqing Li, Kaizhou Gao, Yuyan Han
Abstract
Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migrating birds optimization (IMBO) algorithm for HLFS to minimize makespan. To ensure the diversity of initial population, a Nawaz-Enscore-Ham (NEH) heuristic algorithm is used to generate the leader, and the remaining solutions are randomly generated. According to the characteristics of the HLFS problem, we propose a combined neighborhood search structure that consists of four different neighborhood operators. We design effective local search procedure to explore potential promising domains. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparison demonstrate the feasibility and effectiveness of the proposed algorithm.