"电动车充电网络规划与趋势预测研究"

The study focuses on the transition from gas vehicles to electric vehicles in the context of the increasing popularity of electric vehicles worldwide. The team chose Problem D2018MCM/ICM, which involves constructing an all-electric network including the location of charging stations and predicting the transformation trend. The team's approach involved developing models based on queuing theory and multi-objective programming to address the various aspects of the problem. The queuing theory model was designed to optimize the placement of charging stations in order to minimize wait times for vehicles. By taking into account factors such as the number of vehicles, charging station capacity, and charging time, the model was able to provide insights into the optimal distribution of charging stations within the network. In addition, the team developed a multi-objective programming model to address the complex decision-making process associated with the transition to an all-electric network. By considering multiple objectives such as cost, efficiency, and sustainability, the model was able to generate a comprehensive plan for the transformation process. This model allowed for the comparison of different scenarios and the identification of trade-offs among competing objectives. Overall, the team's research represents a valuable contribution to the field of electric vehicle infrastructure planning. By using a combination of queuing theory and multi-objective programming, the team was able to develop a comprehensive approach to addressing the challenges associated with the transition to an all-electric network. The results of this study have important implications for policymakers, urban planners, and other stakeholders involved in the promotion of sustainable transportation systems.