Amid escalating energy crises and environmental pressures, electric vehicles (EVs) have emerged as an effective measure to reduce reliance on fossil fuels, combat climate change, uphold sustainable energy and environmental development, and strive towards carbon peaking and neutrality goals. This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes (DWCLs) and EV charging strategy joint optimization in highway networks. Taking into account established charging resources in highway service areas (HSAs), the nonlinear charging characteristics of EV batteries, and the traffic capacity constraints of DWCLs. The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs. By ensuring that EVs maintain an acceptable state of charge (SoC), the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment, thus reducing the construction cost of wireless charging facilities and user charging expenses. The efficacy and universality of the model are demonstrated using the classical Nguyen–Dupius network as a numerical example and a real-world highway network in Guangdong Province, China. Finally, a sensitivity analysis is conducted to corroborate the stability of the model. The results show that the operating speed of EVs on DWCLs has the largest impact on total cost, while battery capacity has the smallest. This comprehensive study offers vital insights into the strategic deployment of DWCLs, promoting the sustainable and efficient use of EVs in highway networks.

With the rapid development of warehouse robots in logistics and other industries, research on their path planning has become increasingly important. Based on the analysis of various conflicts that occur when the warehouse robot travels, this study proposes a two-level vehicle path planning model for multi-warehouse robots, which integrates static and dynamic planning to improve operational efficiency and reduce operating costs. In the static phase, the blockage factor is introduced to enhance the ant colony optimization (ACO) algorithm as a negative feedback mechanism to effectively avoid the blockage nodes during movement. In the dynamic stage, a dynamic priority mechanism is designed to adjust the routing strategy in real time and give the optimal path according to the real situation. To evaluate the model’s effectiveness, simulations were performed under different operating environments and application strategies based on an actual grid environment map. The simulation results confirm that the proposed model outperforms other methods in terms of average running distance, number of blocked nodes, percentage of replanned paths, and average running time, showing great potential in optimizing warehouse operations.