Iran Center for Management Studies Journal of Industrial Engineering and Management Studies 2476-308X Articles in Press 2026 05 12 Designing a Perishable Food Supply Chain Model and Analyzing the Financial Risk of Purchase and Distribution 243456 10.22116/jiems.2026.570876.1628 EN Amir Mola Yousefi Department of Industrial Management, NT.C., Islamic Azad University, Tehran, Iran. Vahid Bardaran Industrial Engineering Department, Engineering Faculty, Islamic Azad University, Tehran North Branch, Tehran, Iran 0000-0001-5385-629X Kaveh Khalili-Damghani Department of Industrial Engineering, ST.C., Islamic Azad University, Tehran, Iran. Journal Article 2026 01 05 Perishable food supply chains (PFSCs), particularly in the dairy sector, face significant challenges due to product deterioration, quality degradation, and financial risks associated with distribution delays. Despite extensive research on supply chain optimization, a critical gap remains in accurately modeling the dynamic relationship between product shelf-life and selling price—a factor that significantly impacts revenue and risk assessment in real-world operations. This study addresses this gap by developing a multi-objective optimization model for the dairy supply chain in Iran that incorporates: (1) a novel stepwise pricing mechanism based on remaining shelf-life, capturing revenue loss due to spoilage; (2) financial risk assessment of purchase and distribution operations; (3) transportation planning with vehicle routing; and (4) discount sales policies aligned with product freshness. Given the NP-Hard nature of the problem, NSGA-II and MOPSO algorithms with a modified priority-based encoding-decoding method were employed. Algorithm parameters were systematically tuned using the Taguchi method. The model was validated through a numerical example solved via the LP-metric method, followed by 15 larger test problems to evaluate algorithm performance. Comparative analysis using multiple evaluation metrics—including the number of Pareto-efficient solutions (NPF), maximum spread index (MSI), spacing metric (SM), and computational time—was conducted. The TOPSIS technique was applied to rank algorithm performance, revealing that NSGA-II (weight = 0.6945) significantly outperforms MOPSO (weight = 0.3055) across all problem sizes. The key contributions of this research include: (i) introducing a realistic stepwise pricing function linked to perishability, (ii) integrating financial risk into PFSC optimization, and (iii) providing a robust algorithmic framework for large-scale dairy supply chain problems. These findings offer practical guidance for managers seeking cost-effective, risk-aware, and quality-conscious management of perishable food supply chains.