Using simulation-optimization to integrate financial and product flow planning in supply chains

Researchers have increasingly focused on the impact of financial flow on planning decisions in supply chain networks, especially due to rising costs and concerns about funding and resource allocation in the post-Covid era. This study introduces a simulation optimization model that integrates financial and physical flows in supply chain planning. The model combines mixed-integer linear programming and simulation-based optimization through an iterative process. Financial performance is measured using the economic value added (EVA) index. To evaluate its effectiveness, the proposed model is compared with conventional SBO and MILP approaches on a test problem from recent literature. Results indicate that the proposed simulation-optimization model achieves higher EVA compared to the SBO model and demonstrates greater robustness to economic uncertainty than the MILP approach.