Every year, roughly a third of the world’s perishable food never reaches consumers, not because of demand shortages, but because products spoil in transit and storage. Most supply chain models treat this spoilage as a fixed cost of doing business, relying on rough estimates of shelf life rather than accounting for how temperature actually drives biochemical degradation. This paper takes a different approach. We develop a mixed-integer linear programming (MILP) framework that embeds Arrhenius degradation kinetics, which is the established science of temperature-dependent reaction rates directly into perishable supply chain network design. The key idea is to precompute all Arrhenius-based quality retention factors offline, converting inherently nonlinear thermodynamic relationships into fixed coefficients that keep the optimization model linear and computationally tractable. The framework tracks two quality attributes (firmness and color) as products move through three phases: inbound transport, warehouse storage, and outbound distribution. Delivered quality is then mapped to pricing tiers (premium, standard, discount, or waste), so the model can weigh the cost of cold-chain investment against the revenue it generates. Applied to a Mediterranean tomato case study, the model achieves a 31% profit improvement over conventional cost-only optimization, with zero waste and full demand fulfillment.