GSSA and ACO for assembly sequence planning: A comparative study

Abstract

Assembly sequence planning (ASP) is a crucial design step in the product development process which plays an important role in the fields of CAD/CAM design issues, the cost of assembly/manufacturing, as well as the selection of equipment. Whereas, ASP is an extremely diverse, large scale and highly constrained combinatorial problem, and it is difficult to find an optimal/near-optimal solution in an acceptable time. Numerous researchers have employed soft computing methods such as genetic algorithms (GA) and simulated annealing (SA) algorithms to go towards the assembly sequence features of speed and flexibility. As regards the large constraint assembly problems, however, traditional GAs depend on the initial sequence heavily, which results in the premature convergence in iterative operations. As for SA algorithms, it may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution-searching process. Considering the limitations above, two heuristics algorithms for ASP are presented. The proposed novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for ASP is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity. Furthermore, the GSAA and ACO are applied in a vice ASP, the results of which are compared in respect of the quality of solution and the efficiency of searching process. At last, the advantages and disadvantages of GSAA and ACO are pointed out by comparison, which gives some useful hint on future research.