PSO algorithm for the optimal allocation of retail shelf space

• Main Authors: Hsuan Lu, 陸萱
• Other Authors: 陳彥匡
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/mjgx8b

碩士 === 國立臺中科技大學 === 流通管理系碩士班 === 107 === Object: This study investigates shelf space allocation decision of retail store in a manner that maximizes the overall store profit. We propose particle swarm optimization algorithm for the shelf space model of Yang and Chen (1999) to address the shop shelf space allocation problem. To show the validity of the proposed algorithm in addressing the problem, the results of particle swarm optimization algorithm are compared with results of Yang heuristic (2001), modified heuristic, genetic algorithm and hybrid metaheuristics. Methods: In recent years, population-based metaheuristic algorithms are the most selected to find optimal solution in many areas. There are two distinct forms of population-based metaheuristic algorithms which are evolutionary algorithms and swarm intelligence. Research to date has focus on evolutionary algorithms rather than swarm intelligence. Particle swarm optimization algorithm that belongs to the class of swarm intelligence, an attractive feature of which have few algorithmic parameters, converge fast, short computational time, and so on. Thus, this study presents the investigations on the application of particle swarm optimization algorithm to solve the shelf space allocation problem. Results: We compute the difference between each approach (Yang heuristic (2001), modified heuristic, genetic algorithm, hybrid metaheuristics, particle swarm optimization algorithm) and the best solution of the five approaches. The results show that the particle swarm optimization algorithm performs the best. To verify the validity of particle swarm optimization algorithm, we use Wilcoxon Rank-sum Test statistic. We can conclude from the result that the particle swarm optimization algorithm is a very efficient algorithm. Contributions: Most previous studies of the shelf space allocation decision are rarely refer to swarm intelligence. In this study, we apply the swarm intelligence to solve the shelf space allocation problem. The results show that the performance of swarm intelligence are better than evolutionary algorithms.