Retailing is a complex field that requires accurate and efficient management in various aspects. A retailer’s financial performance depends on its ability to attract consumers to its stores and persuade them to buy. In this regard, we can mention the assortment planning and the shelf space allocation problem as two key and intertwined components of decisions related to a retailer that affect attracting consumers to the store and persuading them to buy. In this research, the integrated problem of determining the store assortment and allocating shelf space at the macro level is investigated. The purpose of this problem is to identify a mix of the fast-¬moving product groups and high¬-impulse product groups to carry to the store and optimize the amount of space allocated to them, as well as determine the horizontal and vertical position to display them on shelves. To do so, we consider a store that consists of a certain number of two-¬dimensional shelves. Each shelf of this store contains a certain number of horizontal and vertical segments with different attractiveness. As one of the main challenges of this problem, the arrangement way of products on the shelves of the store must follow the merchandising rules. These rules consist of a set of instructions in order to optimally arrange the products of a store and increase the productivity of the sales space. Because these rules are based on the expectations and the search way of customers for products while shopping, following these rules in shelf space planning is very important for retailers. After presenting a mathematical programming formulation for the problem, a column generation¬-based heuristic algorithm is designed to solve the problem. This method is equipped with an optimization-¬based algorithm that provides the initial columns for the restricted linear master problem. The performance and efficiency of the proposed solution approach have been eva‎luated using randomly generated instances. The numerical studies show that the proposed algorithm yields high-quality solutions for real¬-sized problem instances with an optimality gap of less than 3.8% in a reasonable time. Further, we demonstrate the usefulness of the proposed methodology by using a case study, which is motivated by real data related to the Ofoq Kourosh chain stores.

مهدي ايران‌پور

مهدي مهنام، فرزاد بهرامي

علي شاهنده نوك آبادي، حميد ميرمحمدي