The Vehicle Scheduling System Based On Cloud Computing

At present,with the expansion of the scale of logistics distribution and the improvement of customers’ requirements for service quality,the complexity of the vehicle scheduling problem has increased exponentially.The swarm intelligence algorithm has unique adaptability in the vehicle scheduling problem,and has received extensive attention from scholars.The purpose of this article is to use an improved swarm intelligence algorithm to solve the vehicle scheduling problem in the logistics field,and to improve the mathematical model of vehicle scheduling to improve the overall satisfaction of logistics distribution services,and to realize that it can be deployed in a cloud environment.Development of vehicle dispatching system in China.In the swarm intelligence optimization algorithm,the ant colony algorithm uses a distributed calculation method in the calculation method,and has good compatibility with other algorithms,and the cuckoo algorithm is also easy to integrate with other algorithms.Hybrid algorithms can combine the excellent mechanisms of the two.Therefore,this paper proposes a hybrid adaptive ant colony algorithm.On the basis of the ant colony algorithm,the Levy flight mechanism of the cuckoo algorithm is integrated to optimize the algorithm’s global search ability and prevent the algorithm’s search process from being premature.Finally,multiple test functions are used to verify the optimization characteristics of the hybrid adaptive ant colony algorithm,and the path optimization test case is used to verify the effectiveness of the hybrid adaptive ant colony algorithm proposed in this paper in dealing with the logistics and distribution vehicle scheduling problem.feasibility.In the case of very limited delivery vehicles,prioritizing customers and providing differentiated services to customers can improve customer satisfaction as a whole.This paper analyzes the customer characteristics and customer priority in the logistics distribution vehicle scheduling problem,introduces the customer priority factor,gives a logistics distribution strategy with priority division,and proposes a route optimization model for the distribution process based on customer priority.This method participates in the route optimization process in a prioritized situation,and designs and provides the best distribution route plan.Finally,the route optimization example in the vehicle scheduling problem is selected for simulation experiment to verify the effectiveness of the model in improving customer satisfaction in the logistics distribution process.Finally,this paper designs and implements a cloud computing-based vehicle scheduling system,which provides a cloud computing environment for the system by building a hadoop cluster,and applies hybrid adaptive ant colony algorithm and vehicle scheduling based on customer priority to the system.The system has the main functions of information management,route optimization,and scheduling.It has passed the basic system test,which verifies that the system meets the design requirements,and further verifies that the algorithms and models proposed in this paper have strong practical value.