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

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