Journal
FRONTIERS OF MECHANICAL ENGINEERING
Volume 16, Issue 2, Pages 393-409Publisher
HIGHER EDUCATION PRESS
DOI: 10.1007/s11465-020-0613-3
Keywords
assembly sequence planning; ant colony optimization; symbiotic organisms search; parameter optimization
Categories
Funding
- National Key R&D Program of China [2018YFB1501302]
- Fundamental Research Funds for the Central Universities, China [2018ZD09, 2018MS039]
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, China
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Assembly sequence planning (ASP) is crucial for product quality and costs, and the SOS-ACO algorithm effectively finds optimal assembly sequences by combining symbiotic organisms search (SOS) and ant colony optimization (ACO). The SOS-ACO algorithm reduces the complexity of parameter assignment and achieves competitive solutions with good adaptive capability.
As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.
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