文献类型：会议论文

英文题名：GSSA and ACO for Assembly Sequence Planning: A Comparative Study

作者：Li Shu-xia[1];Shan Hong-bo[2]

机构：[1]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China

会议论文集：IEEE International Conference on Automation and Logistics

会议日期：SEP 01-03, 2008

会议地点：Qingdao, PEOPLES R CHINA

语种：英文

外文关键词：Assembly sequence planning; Genetic simulated annealing algorithm; Ant colony optimization

摘要：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 rind 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.