Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization

• Main Authors: LIM, Andrew, RODRIGUES, Brian, ZHANG, Xingwen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2004
• Subjects: retail; shelf allocation; metaheuristics; retail industry; Operations and Supply Chain Management; Sales and Merchandising
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/3791; https://ink.library.smu.edu.sg/context/lkcsb_research/article/4791/viewcontent/RodriguesB2004mnscMetaheuristics.pdf
• Institution: Singapore Management University

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.