Optimization of Vehicle Scheduling for Fresh Agricultural Products to be Processed Considering Time-Varying Traffic

During peak periods, traffic congestion often causes large-scale vehicle queuing in the scheduling of fresh agricultural products to be processed. In order to address this vehicle scheduling problem under time-varying traffic conditions, and to investigate the impact of vehicle speed on vehicle queuing and unloading, a time-varying speed characterization model with a multi-peak inverse Gaussian distribution is constructed to describe the impact of traffic conditions on vehicle speed. A time-varying speed optimization model for the transportation of fresh agricultural products to be processed is proposed considering traffic conditions. An improved genetic algorithm that combines a forward continuous crossover operator and an adaptive differential mutation operator is designed. A Gaussian distribution-based queuing time backward estimation mechanism is integrated, and a binary search algorithm is employed to determine the optimal departure time. Results show that the time-varying speed model with the multi-peak inverse Gaussian distribution can effectively capture the time-varying characteristics of simulated vehicle speed. The proposed mathematical optimization model and algorithm can effectively generate vehicle scheduling plans, thereby contributing to the quality of raw material supply and the improvement of logistics efficiency.