TY  - JOUR
T1  - Correlated SKU assignment in warehouses using the joint demand probability distribution: a metaheuristic algorithm approach
AU  - Dündar, Bayram
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.17798/bitlisfen.1714876
JF  - Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
PB  - Bitlis Eren University
WT  - DergiPark
SN  - 2147-3129
SP  - 1772
EP  - 1786
VL  - 14
IS  - 3
LA  - en
AB  - 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%.
KW  - SKUs assignment problem
KW  - demand correlation
KW  - genetic algorithm
KW  - mathematical
modeling
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UR  - https://doi.org/10.17798/bitlisfen.1714876
L1  - https://dergipark.org.tr/en/download/article-file/4938499
ER  -