Optimization of Cold-Chain Logistics Unitization Strategies Under Dynamic Temperature Constraints

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution.