Periodical
Integrated assortment and distribution planning in vendor-managed inventory systems: a clustering-based grouping evolution strategy and adaptive large neighborhood search approach.
| Τίτλος: | Integrated assortment and distribution planning in vendor-managed inventory systems: a clustering-based grouping evolution strategy and adaptive large neighborhood search approach. |
|---|---|
| Συγγραφείς: | Beigi, Sakine1 (AUTHOR), Husseinzadeh Kashan, Ali1,2 (AUTHOR) a.kashan@modares.ac.ir, Ostadi, Bakhtiar2 (AUTHOR), Afsahi, Mohsen2 (AUTHOR) |
| Πηγή: | Computers & Operations Research. Jul2026, Vol. 191, pN.PAG-N.PAG. 1p. |
| Θεματικοί όροι: | *Vendor-managed inventory, *Distribution planning, *Supply chain management, *Retail industry, *Inventory control, Heuristic algorithms, Metaheuristic algorithms, Clustering algorithms |
| Περίληψη: | Effective assortment planning and distribution are critical challenges in retail supply chains, where businesses must balance customer preferences, cost efficiency, and logistical constraints. This study addresses these challenges under a Vendor-Managed Inventory (VMI) framework by introducing a mathematical model that maximizes total profits while incorporating assortment-based substitutions, stock-out substitutions, routing expenses, and an exogenous demand choice model. Given the NP-hard nature of the problem, we propose a heuristic approach that leverages the structural knowledge of the problem through a novel Clustering-based Best Decrease-Increase Search (CBDIS) algorithm. CBDIS combines Randomized Capacitated Regret K-means (RCRKm) clustering with Best Decrease Extra Load (BDEL) and Best Increase Feasible Load (BIFL) methods to optimize resource allocation and routing efficiency. We further enhance it by integrating its components into a Grouping Evolution Strategy (GES) and two Adaptive Large Neighborhood Search (ALNS) variants. A post-optimization technique -Nearest Neighbor Synergistic Refinement (NNSR)- is also introduced for fine-tuning. Tests on generated instances and a real-world case study show that GES achieves superior performance, providing high-quality solutions efficiently. The NNSR refinement further improves solution quality by up to 0.57%, particularly in large-scale instances. Our findings highlight the scalability and robustness of the proposed algorithms, making them viable tools for optimizing retail supply chains under VMI systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Computers & Operations Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrated assortment and distribution planning in vendor-managed inventory systems: a clustering-based grouping evolution strategy and adaptive large neighborhood search approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Beigi%2C+Sakine%22">Beigi, Sakine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Husseinzadeh+Kashan%2C+Ali%22">Husseinzadeh Kashan, Ali</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> a.kashan@modares.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Ostadi%2C+Bakhtiar%22">Ostadi, Bakhtiar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Afsahi%2C+Mohsen%22">Afsahi, Mohsen</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Operations+Research%22">Computers & Operations Research</searchLink>. Jul2026, Vol. 191, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Vendor-managed+inventory%22">Vendor-managed inventory</searchLink><br />*<searchLink fieldCode="DE" term="%22Distribution+planning%22">Distribution planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br />*<searchLink fieldCode="DE" term="%22Retail+industry%22">Retail industry</searchLink><br />*<searchLink fieldCode="DE" term="%22Inventory+control%22">Inventory control</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Effective assortment planning and distribution are critical challenges in retail supply chains, where businesses must balance customer preferences, cost efficiency, and logistical constraints. This study addresses these challenges under a Vendor-Managed Inventory (VMI) framework by introducing a mathematical model that maximizes total profits while incorporating assortment-based substitutions, stock-out substitutions, routing expenses, and an exogenous demand choice model. Given the NP-hard nature of the problem, we propose a heuristic approach that leverages the structural knowledge of the problem through a novel Clustering-based Best Decrease-Increase Search (CBDIS) algorithm. CBDIS combines Randomized Capacitated Regret K-means (RCRKm) clustering with Best Decrease Extra Load (BDEL) and Best Increase Feasible Load (BIFL) methods to optimize resource allocation and routing efficiency. We further enhance it by integrating its components into a Grouping Evolution Strategy (GES) and two Adaptive Large Neighborhood Search (ALNS) variants. A post-optimization technique -Nearest Neighbor Synergistic Refinement (NNSR)- is also introduced for fine-tuning. Tests on generated instances and a real-world case study show that GES achieves superior performance, providing high-quality solutions efficiently. The NNSR refinement further improves solution quality by up to 0.57%, particularly in large-scale instances. Our findings highlight the scalability and robustness of the proposed algorithms, making them viable tools for optimizing retail supply chains under VMI systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers & Operations Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.cor.2026.107417 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Vendor-managed inventory Type: general – SubjectFull: Distribution planning Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Retail industry Type: general – SubjectFull: Inventory control Type: general – SubjectFull: Heuristic algorithms Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Clustering algorithms Type: general Titles: – TitleFull: Integrated assortment and distribution planning in vendor-managed inventory systems: a clustering-based grouping evolution strategy and adaptive large neighborhood search approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Beigi, Sakine – PersonEntity: Name: NameFull: Husseinzadeh Kashan, Ali – PersonEntity: Name: NameFull: Ostadi, Bakhtiar – PersonEntity: Name: NameFull: Afsahi, Mohsen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03050548 Numbering: – Type: volume Value: 191 Titles: – TitleFull: Computers & Operations Research Type: main |
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