Dynamic programming approach for solving the retail shelf-space allocation problem

Abstract The retail sector is an extremely competitive area. One of the most important facets of retailing is managing retail shelf space. In a market place, retailers should offer customers an option of selecting not only the product but also the packaging size and purchasing quantity. In such instances, product prices often differ. This paper investigates a retail shelf-space allocation problem that maximizes the overall planogram profit. The common shelf-space allocation problem was simplified in this research by selecting the shelf on which the items would be placed in advance. This is explained by practical reasons as sometimes retailers assign the products of the specific package, type, brand, price, form or size, weight to the specific shelf. The problem has been formulated as a 0-1 bounded knapsack problem. Exact methods for solving such kinds of a problem include dynamic programming algorithm. We proposed dynamic programming, which could solve the problem using less time and computational resources. The developed dynamic programming could be applied for solving shelf-space allocation problem subproblems or as being a part of other heuristics and metaheuristics approaches. The results of the research are important for the retailers, category managers, and scientists focused on shelf-space allocation or shelf-space optimization problems.