An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem

• Published in: Applied soft computing Vol. 95; p. 106544
• Main Authors: Li, Yibing, Huang, Weixing, Wu, Rui, Guo, Kai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2020
• Subjects: Artificial bee colony (ABC); Dynamic neighborhood search; Flexible job-shop scheduling problem; Low-carbon; Multi-objective optimization; Flexible job-shop scheduling problem; Multi-objective optimization; Dynamic neighborhood search; Artificial bee colony (ABC); Low-carbon
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2020.106544

Based on the analysis of multi-objective flexible job-shop scheduling problem (FJSP), a multi-objective low-carbon job-shop scheduling problem(MLFJSP) with variable processing speed constraint is proposed in this paper. The optimization objectives of MLFJSP include minimizing the makespan, total carbon emission and machine loading. Meanwhile, an improved artificial bee colony algorithm (IABC) is designed to solve the MLFJSP. The improvement of algorithm mainly includes: (1) an effective three-dimensions encoding/decoding mechanism and a mixed initialization strategy are designed to generate a better initial population; (2) special crossover operators and mutation operators were designed to increase the diversity of the population in the employed bee phase; (3)an efficient dynamic neighbor search (DNS) is applied to enhance local search capabilities in the onlooker bee phase; (4) the new food sources generation strategy was proposed to reduce the blindness in the scout bee phase. Finally, this paper carried out a series of comparative experimental studies, including the comparison before and after algorithm improvement, and the comparison between the improved algorithm with MOPSO, MODE and NSGA-II. The results demonstrate that the IABC can achieve a better performance for solving the MLFJSP. •Multi-objective Low-carbon flexible job shop scheduling problem is studied.•Makespan, machine loading and total carbon emission objectives are considered.•An improved artificial bee colony (IABC) algorithm is proposed.•A total of 15 benchmarks are used to test the performance of the IABC.