Route Optimization for Electric Vehicle Cold Chain Delivery Under a Mixed Public–Private Charging Mode: A China-Oriented Case Study

This study addresses the electric refrigerated vehicle routing problem under a mixed public–private charging mode. An optimization model is developed with the objective of minimizing total cost. The model jointly considers vehicle load capacity, battery capacity, customer time windows, refrigeration energy consumption, cargo damage cost, and the heterogeneity of charging resources. To solve this NP-hard problem, an improved Grey Wolf Optimizer is proposed. The algorithm enhances solution quality and convergence performance through elite individual selection, a “destruction–repair” operator, and an adaptive position update strategy. Experimental results based on modified Solomon benchmark instances show that the proposed model can effectively capture the operational characteristics of electric refrigerated distribution under mixed charging scenarios. The proposed IGWO is compared with GA, GWO, and ALNS over multiple independent runs, and the results reported as means ± standard deviations demonstrate its competitive solution quality and robustness. These findings provide theoretical support for optimizing electric cold-chain distribution systems and coordinating charging resources.