Correlated SKU assignment in warehouses using the joint demand probability distribution: a metaheuristic algorithm approach

Year 2025, Volume: 14 Issue: 3, 1772 - 1786, 30.09.2025

Bayram Dündar

https://doi.org/10.17798/bitlisfen.1714876

Abstract

In warehouse management, picking orders from storage locations quickly and in the shortest time has become even more important with the development of e-commerce. Thus, efficiently assigning affined products to storage locations within the warehouses is crucial in reducing operational costs and preserving product quality. In this study, a Mixed-Integer Linear Programming model (MILP) is developed to minimize in-warehouse picking distances. Based on demand data, inter-product relationships are analyzed, and correlation coefficients are estimated for product pairs with a high tendency to be ordered together. These correlation values are then integrated into the objective function to optimize storage location decisions. To obtain faster and near-optimal solutions from the MILP model on large-scale data sets, a genetic algorithm (GA)-based approach has been developed. A set of computational experiments conducted on medium and large-scale instances compares the performance of the proposed GA approach with the Random-Based Correlated Skus Assignment Model (RBC-SAM). The GA approach under different scenarios shows an improvement of up to 22%.

Keywords

SKUs assignment problem , demand correlation , genetic algorithm , mathematical modeling

Ethical Statement

The study is complied with research and publication ethics.

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