Integrated planning for product selection, shelf-space allocation, and replenishment decision with elasticity and positioning effects

As the retail industry is growing larger and more diversified, retailers' decisions about product selection, shelf space-allocation, and replenishment become more important and challenging. This paper is to present a model for shelf-space allocation with product selection and replenishment decisions to maximize the retailer's profit. The model is based on a two-dimensional display space in which all shelves and products have widths and heights and includes factors that influence demand for each product, such as space and cross-space elasticities and positioning effects. The integrated model presented is mixed-integer non-linear programming (MINLP) because the demand function is non-convex. This research proposes two heuristic algorithms (tabu search and genetic) to solve the MINLP problem. The results show the effectiveness and efficiency of these algorithms by comparing the outputs to the MINLP optimal solution for small data sets and comparing the algorithm performances for large data sets. The solution methodologies expect to support a simultaneous decision-making process for retailers to maximize their revenue.