Disassembly sequence planning using a Flatworm algorithm. (October 2020)

• Record Type: Journal Article
• Authors: Tseng, Hwai-En; Huang, Yu-Ming; Chang, Chien-Cheng; Lee, Shih-Chen
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 416
• Page End: 428
• Publication Date: 2020-10
• Subjects: Disassembly sequence planning -- Flatworm algorithm -- Genetic algorithm -- Ant colony algorithm; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.10.014
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A new Flatworm algorithm for disassembly sequence planning model is proposed. Novel growth, fracture and regeneration mechanisms are explored. Solution quality is efficient in improving as complexity of problems increase. Abstract: Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.