Truck scheduling optimization at a cold chain cross-docking terminal considering uncertainties and the door-mixed service mode

The increasing global demand for perishable agricultural products necessitates advancements in cold chain logistics. Cross-docking, known for its efficiency, is particularly well-suited for the transfer and distribution of such goods. However, truck scheduling at cold chain cross-dock terminals (CDTs) presents unique challenges, including product perishability, stringent time windows, and temperature-controlled environments. This work investigates a truck scheduling problem within a cold chain CDT, explicitly addressing uncertainties in refrigerated product damage (affecting supply) and repackaging times. A two-stage stochastic programming model is developed to capture these uncertainties. To solve this model, a scenario reduction approach employing K-means++ and K-medoids clustering is used, followed by Sample Average Approximation. Small-scale instances are solved optimally using CPLEX. For larger instances, a novel hybrid heuristic algorithm, combining the global search capabilities of Genetic Algorithms with the local search capabilities of Adaptive Large Neighborhood Search and Simulated Annealing, is proposed. Numerical experiments demonstrate the effectiveness of this algorithm, and sensitivity analysis provides valuable managerial insights.