The bi-objective mixed-fleet vehicle routing problem under decentralized collaboration and time-of-use prices

Electric vehicles (EVs) can effectively reduce transportation carbon emissions. However, their limited driving range, longer charging times, and scarce charging locations make their transportation efficiency lower compared to traditional internal combustion engine vehicles (ICEVs). A mixed fleet leverages the strengths of both vehicle types. Additionally, collaborative logistics can further enhance these strengths by improving vehicle utilization. Therefore, this study proposes a mixed-fleet model within a collaborative logistics framework to enhance transportation efficiency and balance carbon emission reductions and economic benefits. Considering the variability in charging prices, we developed a bi-objective mixed-fleet vehicle routing optimization model with time windows, incorporating order selection and time-of-use electricity pricing. An ε-constraint clustering hybrid evolutionary algorithm is formulated based on the problem characteristics. Numerical experiments with standard and large-scale instances verified the efficiency and superior performance of the developed model and algorithm. Finally, a sensitivity analysis provided managerial insight.