Multi-objective hybrid algorithms for layout optimization in multi-robot cellular manufacturing systems

•Hybrid algorithms to optimize layouts for multi-robot assembly systems are proposed.•Layout area, operation time and manipulability are the three criteria used.•DE, ABC, CSS and PSO are hybridized with genetic algorithm to have four hybrids.•It is found that GA-PSO hybrid performs better over other...

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Veröffentlicht in:Knowledge-based systems Jg. 120; S. 87 - 98
Hauptverfasser: Lim, Zhen Yang, Ponnambalam, S.G., Izui, Kazuhiro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.03.2017
Elsevier Science Ltd
Schlagworte:
Algorithms
Assembly
Candidates
Cellular manufacture
Charged particles
Classification
Control systems
Cooperation
Design analysis
Design engineering
Dummy
Evolutionary algorithms
Function words
Genetic algorithms
Genetics
Hybrid algorithms
Hybrid systems
Layout
Layout design
Manufacturing
Multi-robot assembly systems
Multiple objective analysis
Multiple robots
Object transportation
Objectives
Optimization
Pareto optimum
Particle swarm optimization
Robotics
Robots
Sorting algorithms
Object transportation
Hybrid algorithms
Layout design
Multi-robot assembly systems
Evolutionary algorithms
ISSN:0950-7051, 1872-7409
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Zusammenfassung:•Hybrid algorithms to optimize layouts for multi-robot assembly systems are proposed.•Layout area, operation time and manipulability are the three criteria used.•DE, ABC, CSS and PSO are hybridized with genetic algorithm to have four hybrids.•It is found that GA-PSO hybrid performs better over other hybrids.•Algorithms are tested using Mitsubishi RV-6SQ robot configuration. Hybrid evolutionary algorithms to optimize layouts for multi-robot cellular manufacturing systems, which includes cooperative tasks among the robots is proposed in this paper. Layout area, operation time and manipulability of robot are the three design criteria useful to evaluate the robotic assembly systems are presented. Layout design candidates are represented using a sequence-pair scheme to prevent interferences between assembly system components, and the introduction of dummy components is proposed to represent layout areas where components are sparse. The main objective of this paper is to propose and evaluate hybrid algorithms by hybridizing them with genetic algorithm, which has been in use for decades. Differential evolution (DE), artificial bee colony (ABC), charged system search (CSS) and particle swarm optimization (PSO) are hybridized with genetic algorithm to have four hybrid (GA+DE, GA+ABC, GA+CSS and GA+PSO) algorithms. The performances of these algorithms are tested with genetic algorithm reported in the literature. The concept of non-dominated sorting genetic algorithm (NSGA-II) is borrowed to handle multiple objectives and to obtain Pareto solutions for the problems considered. These hybrid algorithms are evaluated using an example design problem of multi-robotic assembly system, and the effectiveness of these algorithms are presented in this paper. It is found that GA+PSO performs better over other hybrid algorithms considered. The application of the proposed algorithms are tested using Mitsubishi RV-6SQ robot configuration.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2016.12.026