Hybrid bidirectional ant colony optimization (hybrid BACO): An algorithm for disassembly sequence planning. (August 2019)

• Record Type: Journal Article
• Authors: Tseng, Hwai-En; Chang, Chien-Cheng; Lee, Shih-Chen; Huang, Yu-Ming
• Is Part Of: Engineering applications of artificial intelligence. Volume 83(2019)
• Page Start: 45
• Page End: 56
• Publication Date: 2019-08
• Subjects: Disassembly sequence planning -- Ant colony optimization (ACO) -- Max–min ant system -- Penalty function; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.04.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In traditional disassembly sequence planning (DSP), a disassembly sequence is planned based on the description of the product design and personal experience. Research on disassembly sequence planning needs to take into account the relationship between the parts and related factors, such as changes in orientation and tools, so as to optimize the order of the disassembly sequence. Among the numerous feasible disassembly sequences, it is important for researchers to find the most economical DSP, which becomes more difficult as the number of parts increases. In this study, a hybrid bidirectional ant colony optimization (Hybrid BACO) algorithm is proposed and compared with four related algorithms. Simulated cases show that the hybrid BACO algorithm provides a better solution than other ant algorithms. The five ant algorithms are also compared from the viewpoint of a reverse assembly sequence, and the results again show that the Hybrid BACO algorithm provides the best solution quality. Graphical abstract: Highlights: A novel hybrid bidirectional ant colony algorithm for disassembly planning is explored. A BACO divides the ants into two groups and two routes of the algorithm are integrated. The proposed algorithm performs better than other ant systems in solution quality.