Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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] |
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| Βάση Δεδομένων: |
Business Source Index |