Article ID: | iaor20073260 |
Country: | United States |
Volume: | 50 |
Issue: | 1 |
Start Page Number: | 117 |
End Page Number: | 131 |
Publication Date: | Jan 2004 |
Journal: | Management Science |
Authors: | Lim Andrew, Rodrigues Brian, Zhang Xingwen |
Keywords: | heuristics: local search |
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.