NDLI: GSSA and ACO for assembly sequence planning: A comparative study

Content Provider	IEEE Xplore Digital Library
Author	Li Shu-xia Shan Hong-bo
Copyright Year	2008
Description	Author affiliation: Sch. of Bus., East China Univ. of Sci. & Technol., Shanghai (Li Shu-xia)
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.
Starting Page	1270
Ending Page	1275
File Size	217959
Page Count	6
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636347
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Gallium Genomics Trajectory Planning Bioinformatics Assembly Assembly sequence planning Genetic simulated annealing algorithm Convergence
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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