Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.