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

Abstract

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.