Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

•The multi-objective FFJSP with two objectives is considered.•Problem-specific initial heuristic and VNS are designed.•A self-adaptive MOEA/D is proposed.•The results indicate the superior performance of our approach. With increasing environmental awareness and energy requirement, sustainable manufa...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers & industrial engineering Ročník 168; s. 108099
Hlavní autori: Li, Rui, Gong, Wenyin, Lu, Chao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.06.2022
Predmet:
Fuzzy flexible job shop scheduling
Multi-objective optimization
Parameter self-adaptation
Triangular fuzzy number
Parameter self-adaptation
Multi-objective optimization
Fuzzy flexible job shop scheduling
Triangular fuzzy number
ISSN:0360-8352, 1879-0550
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•The multi-objective FFJSP with two objectives is considered.•Problem-specific initial heuristic and VNS are designed.•A self-adaptive MOEA/D is proposed.•The results indicate the superior performance of our approach. With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, there is a high level of uncertainty in practical processing procedure, particularly in flexible manufacturing systems. This study addresses the multi-objective flexible job shop scheduling problem with fuzzy processing time (MOFFJSP) to minimize the makespan and the total workload simultaneously. A mixed integer liner programming model is presented and a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) is proposed to handle this problem. HPEA has the following features: (i) two problem-specific initial rules considering triangular fuzzy number are presented for hybrid initialization to generate diverse solutions; (ii) five problem-specific local search methods are incorporated to enhance the exploitation; (iii) an effective solution selection method based on Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; and (iv) a parameter selection strategy is proposed to improve the quality of non-dominated solutions. To verify the effectiveness of HPEA, it is compared against other well-known multi-objective optimization algorithms. The results demonstrate that HPEA outperforms these five state-of-the-art multi-objective optimization algorithms in solving MOFFJSP.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2022.108099