This study presents a novel integration of Arrhenius kinetics with perishable food supply chain network optimization to address quality degradation in temperature-sensitive products. Traditional supply chain models oversimplify quality decay using linear approximations, failing to capture the exponential temperature-deterioration relationship observed in food science. We develop a mixed-integer linear programming model incorporating pre-calculated Arrhenius-based quality factors, enabling scientific temperature-dependent decision making while maintaining computational tractability. Using a synthetic Mediterranean tomato supply chain, the model optimizes warehouse locations, vehicle assignments, and quality-based pricing strategies. Results demonstrate that scientific quality modeling achieves 95% quality retention across all pathways, 75% optimal refrigerated transport ratio, and $29,149.88 weekly profit, representing 31% improvement over traditional cost-only optimization. The Arrhenius integration provides quantitative justification for cold chain infrastructure investment, with refrigerated transport yielding $1.21/ton price premium. Implementation validates both theoretical accuracy and practical value, demonstrating 95.0% vs 94.9% quality retention for refrigerated and ambient pathways respectively. This research bridges thermodynamic principles with operational optimization, establishing a new paradigm for quality-centric supply chain management and offering practitioners a scientifically-grounded framework for sustainable supply chain design.