Climate-adaptive UAV delivery for perishable and valuable products: routing and cost optimization strategies based on spoilage kinetics

Unmanned Aerial Vehicles (UAVs) offer a promising solution for the last-mile delivery of perishable products. However, in scenarios characterized by high ambient temperatures or a lack of cold-chain infrastructure, existing routing models often fail to address post-harvest losses driven by heat accumulation, limiting the economic viability of the supply chain. This study proposes a comprehensive single-objective UAV routing model introducing Arrhenius quality kinetics into multiple cost considerations. The model is supplemented with non-linear spoilage costs to achieve optimization and employs a genetic algorithm (GA) to solve the mixed-integer non-linear programming (MINLP) problem. A comparative analysis was conducted between the new model and a conventional "energy-first" model with No Spoilage Cost to illustrate the model’s effectiveness. Results reveal a critical "Energy-Freshness Trade-off" mechanism: under high temperatures, the model incurs marginally higher energy costs (by payload-heavy routing) to secure significant savings in product value. Specifically, at 35°C, the proposed strategy reduces the average total operational cost by approximately 5.78% compared to the baseline. This study demonstrates that for temperature-sensitive agro-products, shifting operational priority from "energy minimization" to "freshness preservation" is an effective strategy to cope with high-temperature climates.