Optimizing Gondola Replenishment: A Simulation Approach Using Promodel and Evolutionary Algorithms to Balance Costs

Abstract

Processes within the supermarket retail sector necessitate continuous enhancements to minimize expenditures and maintain competitiveness. This research endeavors to develop a model and simulate the gondola replenishment process at a point of sale located in Guayaquil, Ecuador. The modeling incorporates relevant factors such as merchandise restocking to estimate the personnel required for adequate product replenishment. The simulation methodology allows for modeling a point of sale through various merchandise supply scenarios and estimating the optimal number of personnel needed. The simulation and its components are established based on observed processes, with merchandise boxes identified as the primary unit of measure within the model, and data asymmetry evaluated using probability distributions. This modeling is executed using queuing theory and evolutionary algorithms via Promodel software, with the SimRunner optimizer used to identify the optimal staffing levels. Given the complexities associated with mathematical programming and specialized software such as the General Algebraic Modeling System, this research presents an accessible and readily applicable alternative solution for planning managers.