A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

•Multi-objective fuzzy disassembly line balancing problem is studied.•An improved Pareto based artificial fish swarm algorithm is proposed.•The Pareto set provides the diversity of non-inferior solutions.•Comparison with an existing algorithm proves the superiority of the proposed method.•The practi...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems with applications Ročník 86; s. 165 - 176
Hlavní autori: Zhang, Zeqiang, Wang, Kaipu, Zhu, Lixia, Wang, Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 15.11.2017
Elsevier BV
Predmet:
Algorithms
Animal behavior
Artificial fish swarm algorithm
Disassembly line balancing problem
Disassembly tasks
Dismantling
Fish
Genetic algorithms
Mathematical models
Multi-objective optimization
Multiple objective analysis
Pareto optimal solutions
Swarm intelligence
Triangular fuzzy number
Pareto optimal solutions
Disassembly line balancing problem
Multi-objective optimization
Artificial fish swarm algorithm
Triangular fuzzy number
ISSN:0957-4174, 1873-6793
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•Multi-objective fuzzy disassembly line balancing problem is studied.•An improved Pareto based artificial fish swarm algorithm is proposed.•The Pareto set provides the diversity of non-inferior solutions.•Comparison with an existing algorithm proves the superiority of the proposed method.•The practicality of the proposed method is confirmed through an actual instance. To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.05.053