Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization

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.

• 저널Management Science · 50(1) · 117–131
• 토픽Consumer Market Behavior and Pricing · Marketing
• DOI10.1287/mnsc.1030.0165
• 저자Andrew Lim, Brian Rodrigues, Xingwen Zhang