Research On Dynamic Vehicle Scheduling Problem Based On Hybrid Quantum Algorithm

With the vigorous development of e-commerce,logistics has become an indispensable industry and the source of benefits.But today’s social logistics and distribution system delivery has not yet reached the interests of maximizing the time to minimize the goal.In order to solve this problem,the problem of vehicle scheduling in logistics distribution system has become a hot topic in academia.Although the academic research on the problem of logistics distribution is different,but its purpose is to get closer to the reality of the distribution model based on the maximum economic benefits,the minimum cost,minimize the time,the highest customer evaluation reputation.The problem of vehicle scheduling in logistics distribution system is a problem of NP,and the algorithm is often used as a traditional heuristic algorithm.However,heuristic algorithms also have some limitations,such as slow convergence,easy to fall into local optimum,poor global search ability and low accuracy.With the complexity of vehicle scheduling mathematical model,the traditional heuristic algorithm is more difficult to get the ideal optimization results.In this paper,we focus on different types of dynamic vehicle scheduling problems,and improve the design of hybrid quantum algorithms,and use the designed algorithm to simulate and compare experiments to verify the effectiveness and superiority of the algorithm in solving specific problems,the main work is as follows:Firstly,the mathematical model of dynamic vehicle scheduling is proposed.Combining quantum computing with genetic algorithm to form quantum ant colony algorithm.The adaptive quantum revolving door is designed according to the fitness value,and the two elements local search is added to improve the local search ability of the algorithm.Finally,the simulation results show that the proposed algorithm is superior to other algorithms,and the effectiveness of the adaptive quantum revolving door,mutation operation and local search of two elements are given.Secondly,a multi-distribution center is added on the basis of the dynamic vehicle scheduling model.At the same time,the ant colony algorithm is combined with the quantum computation to form the quantum ant colony algorithm,and the adaptive quantum revolving door is used instead of the conventional quantum revolving door.The local search function is added to the algorithm to improve the local search ability of the algorithm.Finally,the experimental results show that the proposed algorithm is superior to other algorithms in solving the specific problem,and the effectiveness of the two-element local search by the adaptive quantum revolving gate.Finally,we add multi-vehicle factors to multi-vehicle dynamic vehicle scheduling problem model.At the same time,due to the complication of vehicle scheduling problem,pheromone matrix mutation operation is added to the adaptive quantum ant colony algorithm to improve the search depth of the algorithm.Finally,the experimental results show that the proposed algorithm is superior to other algorithms in solving the specific problem,and the effectiveness of the pheromone mutation operation.In this paper,the hybrid quantum algorithm and the dynamic vehicle scheduling problem are studied,and the characteristics of the hybrid quantum algorithm are summarized,which establishes the foundation for the research of the subsequent vehicle scheduling problem.