Assembly sequence planning of rigid and flexible parts

• Published in: Journal of manufacturing systems 2015-07, Vol.36, p.128-146
• Main Authors: Ghandi, Somayé, Masehian, Ellips
• Format: Article
• Language: English
• Subjects: Assembly sequence planning (ASP); Assembly stress matrix (ASM); Flexible assembly parts; Parameter tuning; Scatter search; TOPSIS method
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2015.05.002

•Deformability conditions considered in Assembly Sequence Planning for the first time.•A new tool (ASM) is developed for modeling assemblies with rigid and flexible parts.•Scatter Search is used for optimizing assembly sequences of some benchmark problems. Assembly sequence planning (ASP) is the process of computing a sequence of assembly motions for constituent parts of an assembled final product. ASP is proven to be NP-hard and thus its effective and efficient solution has been a challenge for the researchers in the field. Despite the fact that most assembled products like ships, aircrafts and automobiles are composed of rigid and flexible parts, no work exists for assembly/disassembly sequence planning of flexible parts. This paper lays out a theoretical ground for modeling the deformability of flexible assembly parts by introducing the concept of Assembly stress matrix (ASM) to describe interference relations between parts of an assembly and the amount of compressive stress needed for assembling flexible parts. Also, the Scatter Search (SS) optimization algorithm is customized for this problem to produce high-quality solutions by simultaneously minimizing both the maximum applied stress exerted for performing assembly operations and the number of assembly direction changes. The parameters of this algorithm are tuned by a TOPSIS-Taguchi based tuning method. A number of ASP problems with rigid and flexible parts were solved by the presented SS and other algorithms like Genetic and Memetic algorithms, Simulated Annealing, Breakout Local Search, Iterated Local Search, and Multistart Local Search, and the results and their in-depth statistical analyses showed that the SS outperformed other algorithms by producing the best-known or optimal solutions with highest success rates.