Using memetic algorithms with guided local search to solve assembly sequence planning

The goal of assembly planning consists in generating feasible sequences to assemble a product and selecting an efficient assembly sequence from which related constraint factors such as geometric features, assembly time, tools, and machines are considered to arrange a feasible assembly sequence based on planner's individual heuristics. Suchlike planning may implement genetic algorithms to go towards the assembly sequence features of speed and flexibility. As regards the large constraint assembly problems, however, traditional genetic algorithms will generate a great deal of infeasible solutions in the evolution process which results in inefficiency of the solution-searching process. Guided genetic algorithms proposed by Tseng, then, got over the restrictions of traditional GAs by means of a new evolution procedure. However, Guided-GAs dealt with the assembly sequence problem in the feasible solution range simply. They were consequently inclined to lapse into the local optimal situation and fall short of the expectations. This paper attempts to add global search algorithms not only based on GAs but also treated of the Guided-GAs as the local search mechanism. The proposed novel method under the name of memetic algorithms for assembly sequence planning is possessed of the competence for detecting the optimal/near-optimal solution with respect to large constraint assembly perplexity. Also, actual examples are presented to illustrate the feasibility and potential of the proposed MAs approach. It has been confirmed that MAs satisfactorily provide superior solutions for assembly sequence problems on the strength of comparison with Guided-GAs. (c) 2006 Elsevier Ltd. All rights reserved.