Optimizing Electric Cold-Chain Vehicle Scheduling for Sustainable Urban Logistics: A Novel Framework Balancing Freshness and Vehicle Charging

Cold-chain logistics, characterized by high energy consumption and significant emissions, pose a critical challenge for the green transformation of global transportation. Electric cold-chain vehicles have emerged as a promising solution to reduce carbon emissions in urban logistics. However, their scheduling is highly complex due to the need to balance freshness and charging requirements, presenting operational challenges for cold-chain companies. To address this issue, this paper proposes an optimization model and algorithm for the efficient scheduling of these innovative electric cold-chain vehicles. First, we define the unique features of these vehicles and establishes an operational framework tailored to cold-chain logistics. Subsequently, we develop a mixed-integer programming model to optimize freshness preservation. Additionally, we design a state-of-the-art algorithm based on an improved genetic algorithm to solve the scheduling model effectively. Numerical experiments conducted using operational data from Shanghai, China, validate the proposed method and algorithm. This study provides valuable insights and tools to support the green transformation of urban cold-chain logistics and contributes to the reduction of urban carbon emissions.