Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots

Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been researched and applied for numerous optimization problems. In this study, we propose an improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduli...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Swarm and evolutionary computation Ročník 52; s. 100600
Hlavní autori: Li, Jun-qing, Tao, Xin-rui, Jia, Bao-xian, Han, Yu-yan, Liu, Chuang, Duan, Peng, Zheng, Zhi-xin, Sang, Hong-yan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.02.2020
Predmet:
Hybrid flowshop
Lot-streaming scheduling
Multi-objective optimization
Variable sub-lots
Multi-objective optimization
Lot-streaming scheduling
Hybrid flowshop
Variable sub-lots
ISSN:2210-6502
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been researched and applied for numerous optimization problems. In this study, we propose an improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduling (HFS) lot-streaming problems, where the variable sub-lots constraint is considered to minimize four objectives, i.e., the penalty caused by the average sojourn time, the energy consumption in the last stage, as well as the earliness and the tardiness values. For solving this complex scheduling problem, each solution is coded by a two-vector-based solution representation, i.e., a sub-lot vector and a scheduling vector. Then, a novel mutation heuristic considering the permutations in the sub-lots is proposed, which can improve the exploitation abilities. Next, a problem-specific crossover heuristic is developed, which considered solutions with different sub-lot size, and therefore can make a solution feasible and enhance the exploration abilities of the algorithm as well. Moreover, several problem-specific lemmas are proposed and a right-shift heuristic based on them is subsequently developed, which can further improve the performance of the algorithm. Lastly, a population initialization mechanism is embedded that can assign a fit reference vector for each solution. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms, both in solution quality and population diversity. •A lot-streaming HFS problem with a variable number of sub-lots is considered.•A right-shift strategy considering the problem characteristics is developed.•A problem-specific crossover heuristic is developed.•A novel mutation heuristic considering the permutations in the sub-lots is proposed.
ISSN:2210-6502
DOI:10.1016/j.swevo.2019.100600