Integrated Supply Chain–Finance Optimization Using Mixed Integer Programming: A Comprehensive Analysis
DOI:
https://doi.org/10.32628/IJSRST25126503Keywords:
Integrated supply chain–finance optimization, Mixed Integer Programming (MIP), Multi-objective optimization, Supply chain finance (SCF), Conditional Value-at-Risk (CVaR), Stochastic programming under uncertaintyAbstract
This study develops an integrated Mixed Integer Programming (MIP) framework for simultaneous optimization of supply chain design and financial performance. Unlike traditional models that decouple operational and financial decision-making, the proposed Integrated Supply Chain–Finance Optimization (ISFO) framework embeds Net Present Value (NPV), working capital constraints, and financial risk measures directly into strategic and tactical supply chain optimization. A multi-objective formulation enables structured analysis of profitability–risk trade-offs, while scenario-based stochastic programming captures demand, supply, and financial uncertainty. The results demonstrate that operational design decisions significantly alter liquidity exposure, capital structure, and long-term firm value. The study contributes a unified modeling architecture that enhances cross-functional integration between operations and finance, offering both theoretical advancement and practical decision-support relevance for industrial-scale supply chains.
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