A breakout local search (BLS) method for solving the assembly sequence planning problem. (March 2015)

• Record Type: Journal Article
• Authors: Ghandi, Somayé; Masehian, Ellips
• Is Part Of: Engineering applications of artificial intelligence. Volume 39(2015:Mar.)
• Page Start: 245
• Page End: 266
• Publication Date: 2015-03
• Subjects: Assembly Sequence Planning (ASP) -- Fitness landscape analysis -- Breakout local search -- Multi-objective optimization -- Statistical analysis; 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.2014.12.009
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Being one of the main subproblems of the broader Assembly Planning (AP) problem, Assembly Sequence Planning (ASP) is defined as the process of computing a sequence of assembly motions for constituent parts of an assembled final product. ASP is proven to be NP-complete and thus its effective and efficient solution has been a challenge for the researchers in the field. However, despite the existence of numerous works on solving the ASP, the topology, structure, and complexity of the problem׳s search space (i.e., the fitness landscape) has not been studied yet. In this article, the fitness landscape of the ASP problem is analyzed for five typical assembled products through various distribution and correlation statistical measures, which reveals that locally optimal assembly sequences are distributed in the problem׳s landscape nearly uniformly. Based on this result, a suitable optimization algorithm called Breakout Local Search (BLS) is selected and customized for obtaining high-quality solutions to ASP. A number of ASP problems are solved by the presented BLS and other algorithms in the ASP literature, including simulated annealing, genetic algorithm, memetic algorithm, harmony search, hybrid immune systems-particle swarm optimization, as well as by other variants of local search like iterated local search and multi-start local search. Experimental results and their in-depth statistical analyses show that the BLS outperforms other algorithms by producing the best-known or optimal solutions most of the time.