Swarm Intelligence Approaches to Shelf Space Allocation Problem with Linear Profit Function

Abstract

The efficient management of shelf space carries critical importance on both the reduction of operational costs and improvement of financial performance. In this context, which products to display among the available products (assortment decision), how much shelf space to allocate the displayed products (allocation decision) and which shelves to display of each product (location decision) can be defined as main problems of shelf space management. In this paper, allocation problem of shelf space management is examined. To this end, a model which includes linear profit function is used for the shelf space allocation decision. Then, heuristic approaches are developed based on particle swarm optimization and artificial bee colony for this model. Finally, the performance analysis of these approaches is realized with problem instances including different number of products and shelves. Experimental results show that the proposed swarm intelligence approaches are superior to Yang's heuristics for the shelf space allocation model.