A Novel Green Logistics Vehicle Scheduling Method Against Road Congestion Utilizing Vehicle–Road–Cloud Collaborative Technology

Abstract

In modern urban logistics and schedule systems, road congestion stands out as a primary contributor to heightened energy consumption in new energy logistics vehicles. Addressing this issue, this study establishes a scheduling method for new energy logistics vehicles comprising several key components: Using the vehicle–road–cloud collaborative technology, the number of vehicles on the road is obtained, and the road congestion coefficient is calculated by combining the speed-flow model, and then the nonlinear energy consumption model for new energy logistics vehicles is studied. Additionally, a VRC-GVRP model is developed considering multiple constraints with the aim of minimizing total energy consumption. To solve this model, an initial solution is constructed using an energy-saving algorithm, while exploring a Cauchy variational strategy and a parallel local search to propose an improved adaptive large neighborhood search (ALNS) algorithm. An illustrative analysis is conducted within an industrial park, based on the real-time traffic information aggregated to the cloud control platform, and the scheduling problem of new energy logistics vehicles is solved. The experimental results indicate that the enhanced ALNS algorithm exhibits rapid convergence and yields high-quality solutions. Compared to the situation without vehicle–road–cloud collaboration technology, despite the increase in the total distance traveled by new energy logistics vehicles, the proposed method effectively reduces total drive time and total energy consumption. As the congestion factor increases, the percentage of reduction in total time and total energy consumption becomes higher and higher, indicating that this method is of great significance for improving the work efficiency of new energy logistics vehicles and achieving energy conservation and emission reduction.