基于Q学习超启发算法的混合车队同时取送货问题研究OA

Considering factors such as energy constraints of electric vehicles,dynamic energy consumption during vehicle operation,and time window of customers,the routing and distribution optimization of a heterogeneous fleet for simultaneous pickup and delivery becomes increasingly complex and challenging.To address this problem,a bi-objective mixed-integer optimization model is developed with the goals of minimizing operating costs and maximizing customer satisfaction.A method integrating the Q-learning mechanism and the super-heuristic search framework is proposed,and the problem fea-ture knowledge is utilized to guide the generation of high-quality initial solutions,thereby improving the solution efficiency.Numerical experiments are conducted to validate the proposed model and algorithm.The results show that there is a con-flict between the operational cost objective and the customer satisfaction objective in the model.The proposed algorithm can be configured and run in various types of examples,and can cover a wider target space while ensuring the uniform dis-tribution of the solution set.Compared with traditional methods,the proposed algorithm effectively balances the two goals of operating costs and customer satisfaction.Research shows that the proposed model and algorithm are applicable to distribution scenarios of different scales,providing new theoretical support and optimization tools for enterprises to reduce operating costs and enhance customer satisfaction.