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INTEGRATED COST OPTIMIZATION IN SUPPLY CHAINS: A MIXED INTEGER LINEAR PROGRAMMING APPROACH

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Název: INTEGRATED COST OPTIMIZATION IN SUPPLY CHAINS: A MIXED INTEGER LINEAR PROGRAMMING APPROACH
Autoři: Sindhu. R. Dhavale, Gajanan. C. Lomte, Omprakash S. Jadhav
Zdroj: Journal of Dynamics and Control. 9:54-70
Informace o vydavateli: PNAD Digital Publishers, 2025.
Rok vydání: 2025
Popis: This Paper presents an integrated supply chain optimization model designed to minimize total costs in supply chain management using a Mixed Integer Linear Programming (MILP). The primary objective is to reduce overall transportation costs within the supply chain, including costs at each stage and inventory costs at warehouses. The model also ensures customer demands are met while considering the capacity constraints of suppliers, manufacturers, warehouses and customer zones. The supply chain in this Paper includes four echelons: supplier, manufacturer, warehouse and customer. Specifically, it comprises six suppliers, two manufacturers, five warehouses and five customer locations. By integrating these elements into a cohesive optimization model, businesses can make data-driven decisions to enhance supply chain efficiency and lower overall costs.
Druh dokumentu: Article
DOI: 10.71058/jodac.v9i4005
Přístupové číslo: edsair.doi...........1c3621df70f5a3914de65d50ba6b01b9
Databáze: OpenAIRE
Popis
Abstrakt:This Paper presents an integrated supply chain optimization model designed to minimize total costs in supply chain management using a Mixed Integer Linear Programming (MILP). The primary objective is to reduce overall transportation costs within the supply chain, including costs at each stage and inventory costs at warehouses. The model also ensures customer demands are met while considering the capacity constraints of suppliers, manufacturers, warehouses and customer zones. The supply chain in this Paper includes four echelons: supplier, manufacturer, warehouse and customer. Specifically, it comprises six suppliers, two manufacturers, five warehouses and five customer locations. By integrating these elements into a cohesive optimization model, businesses can make data-driven decisions to enhance supply chain efficiency and lower overall costs.
DOI:10.71058/jodac.v9i4005