Meta-Heuristics with Local Search for Retail Shelf Allocation 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. [PUBLICATION ABSTRACT]
retail, shelf allocation, metaheuristics
Operations and Supply Chain Management
LIM, Andrew; Rodrigues, Brian; and ZHANG, Xingwen.
Meta-Heuristics with Local Search for Retail Shelf Allocation Optimization. (2004). Management Science. 50, (1), 117-131. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/2279