A Simulation-optimization Approach for Replenishment Planning and Shelf Space Allocation in Retail Industry under Zone- and Stock-dependent Demand

Abstract

A<br />simulation-optimization approach is proposed for replenishment planning and<br />shelf space allocation. The customers' demand is divided into fixed and<br />variable demand. Each customer follows a given path while shopping in the<br />store. By dividing the store space into several zones and calculating the<br />related substantial and random utilities, an analytical equation is proposed<br />for the probability of zone visits. Due to the complexity of the proposed<br />equation, a simulation method is used to evaluate the customers' movements and<br />predict the variable demand. A profit-based integer-programming model is then<br />formulated for the case problem that needs the output demand of the<br />simulation phase. However, the optimal values of some decision variables should<br />be available through simulation. Therefore, a simulation-optimization algorithm<br />is iteratively run since a pre-determined small deviation from the expected<br />profit goal is achieved. Finally, the numerical results are reported for a<br />small-sized problem.