Optimizing agri-food supply chain configuration for mitigating both supply- and demand-side risks

Agri-food supply chains are increasingly vulnerable to disruptions from climate change, geopolitical conflicts, and pandemics, which manifest as both supply-side (e.g., volatile lead times) and demand-side uncertainties, threatening profitability and food security. While existing research has addressed these risks in isolation, a critical gap exists in tactically configuring perishable food supply chains to jointly mitigate both types of risk through integrated decision-making on sourcing, logistics, and inventory. This paper develops a novel Food Supply Chain Configuration Problem under Supply–Demand Risks (FSCCP-SDR). It is the first optimization model to simultaneously determine operational mode selection (supplier, transport, processing) and multi-echelon inventory placement (safety stock and early arrival stock) for perishable products under both stochastic demand and stochastic lead time, maximizing total net profit. To solve this NP-hard problem, we propose an exact dynamic programming method for small instances and a hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) with a custom repair mechanism for scalability. Applied to a grape processor case study in China, the hybrid PSO-GA finds near-optimal solutions (within 0.57% of optimal) efficiently. The model yields non-intuitive, data-driven configurations, such as selecting low-cost, high-loss options upstream and prioritizing reliability downstream. Sensitivity analyses reveal: (1) increased supply-side risk elevates both safety and early arrival stock, justifying investments in supplier reliability; (2) extended shelf-time significantly reduces required inventory, quantifying the value of preservation technologies; and (3) under extreme perishability, the optimal strategy pivots from "buffering with inventory" to "buffering with responsiveness." The methodology demonstrates strong scalability on larger synthetic networks.