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

Bayram Dündar

Makale detayları

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

Yazarlar:

Bayram Dündar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Packaging | Storage and Transportation (Excl. Food and Agricultural Products)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1772-1786

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359264

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Bayram Dündar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Packaging | Storage and Transportation (Excl. Food and Agricultural Products)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1772-1786

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359264

pISSN:

2147-3129

SKUs assignment problem

demand correlation

genetic algorithm

mathematical modeling

Sayı

Makale detayları

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

Yazarlar: Bayram Dündar

Yayıncı: Bitlis Eren University

Ana Başlıklar: Packaging | Storage and Transportation (Excl. Food and Agricultural Products)

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1772-1786

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001359264

pISSN: 2147-3129

SKUs assignment problem

demand correlation

genetic algorithm

mathematical modeling

Sayı

Yazarlar:

Bayram Dündar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Packaging | Storage and Transportation (Excl. Food and Agricultural Products)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1772-1786

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359264

pISSN:

2147-3129