A Narrative Review: Optimization Algorithms for The Problem of Allocating Shelf Space in Retailing

Abstract

The Shelf Space Allocation Problem (SSAP) is of great significance in the retail industry since it directly impacts the effectiveness of product placement and organization within a shop. This issue arises from a deficiency in comprehending the strategic arrangement of products, resulting in the wasteful utilization of space. Notably, SSAP has a significant negative impact on store operations and customer satisfaction, resulting in wasted space and a reduction in the number of products that can be displayed to customers. Hence, the careful selection of an optimization method is crucial for effectively managing the allocation of shelf space in retail operations. This study aims to examine optimization algorithm techniques for addressing the issue of shelf space allocation in a particular environment, such as enhancing sales, minimizing congestion, or optimizing space utilization. Several studies have considered several optimization algorithm methods that have been used in solving the SSAP, including Genetic Algorithm (GA), Linear Programming (LP), Dynamic Programming (DP), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS). These methods are selected based on problem characteristics, including nonlinearity, integer requirements for variables, and constraints such as limited rack space. This optimization method helps retailers to effectively optimize shelf space allocation and maximize sales and profits. The study thus reviewed previous papers examining optimization algorithm methods to solve the problem of shelf space allocation. Those methods are crucial in improving product organization efficiency, customer satisfaction, and overall business performance.