Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization

Andrew Lim (), Brian Rodrigues () and Xingwen Zhang ()
Additional contact information
Andrew Lim: Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Brian Rodrigues: School of Business, Singapore Management University, 469 Bukit Timah Road, Singapore 259756
Xingwen Zhang: Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

Management Science, 2004, vol. 50, issue 1, 117-131

Abstract: Efficient shelf-space allocation can provide retailers with a competitive edge. While there has been little study on this subject, there is great interest in improving product allocation in the retail industry. This paper examines a practicable linear allocation model for optimizing shelf-space allocation. It extends the model to address other requirements such as product groupings and nonlinear profit functions. Besides providing a network flow solution, we put forward a strategy that combines a strong local search with a metaheuristic approach to space allocation. This strategy is flexible and efficient, as it can address both linear and nonlinear problems of realistic size while achieving near-optimal solutions through easily implemented algorithms in reasonable timescales. It offers retailers opportunities for more efficient and profitable shelf management, as well as higher-quality planograms.

Keywords: retail; shelf allocation; metaheuristics (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (42)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:50:y:2004:i:1:p:117-131

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