Artificial Particle Swarm Optimization for Solving Single Row Facility Layout Problem

• Main Authors: Amalia Utamima, 英妲
• Other Authors: Chao Ou-Yang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/61009424214185012459

碩士 === 國立臺灣科技大學 === 工業管理系 === 100 === The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (SRFLP). The objective of SRFLP, categorized as NP-Complete problem, is to arrange the layout so that the sum of distances between all facilities’ pairs can be minimized. Estimation Distribution Algorithm (EDA) improves the solution quality efficiently in first few runs, but the diversity lost grows rapidly as more iterations are run. To maintain the diversity, hybridization with meta-heuristic algorithms is needed. This research proposes Artificial Particle Swarm Optimization (APSO), an algorithm which consists of hybridization of EDA, Particle Swarm Optimization (PSO), and Tabu Search (TS). Other hybridization algorithms are also built as comparers. They are extended Artificial Chromosomes Genetic Algorithm (eACGA), Estimation Distribution Algorithm Particle Swarm Optimization (EDAPSO), and Estimation Distribution Algorithm Tabu Search (EDAtabu). APSO’s performance is tested in 15 benchmark problems of SRFLP and it successfully achieves optimum solution. Moreover, the mean error rates of APSO always get the lowest value compared to other algorithms. SRFLP can be enhanced by considering more constraints and become enhanced SRFLP. Computational results show that APSO also can solve enhanced SRFLP effectively. Therefore, we can conclude that APSO is a promising meta-heuristic algorithm which could be used to overcome the basic and enhanced SRFLP.