Ant colony optimization for assembly sequence planning based on parameters optimization

Abstract

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.

Graphical abstract

Keywords

assembly sequence planning / ant colony optimization / symbiotic organisms search / parameter optimization

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Acknowledgements

This work was supported by the National Key R&D Program of China (Grant No. 2018YFB1501302) and the Fundamental Research Funds for the Central Universities, China (Grant Nos. 2018ZD09 and 2018MS039). It is also supported by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, China.

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