Research On Vehicle Scheduling Model And Optimization Under Partial Collaborative Transportation Strategy

Modern logistics industry is a crucial pillar of the modern service industry.Currently,total cost of logistics in Gross National Product(GDP)was reduce to 14 percent,much higher than the average of 10 percent in developed conutry.At present,low vehicle utilization and high logistics cost are closely related to the rate of empt driving.Inefficient vehicle scheduling and optimization also cause roundabout transportation and resources idle problems.In recent years,the country has issued a series of policies and mesures which are favorable to the logitics industrial transformation and high-quality development.Now the research on vehicle routing problem has more focused on the algorithms,it is the practice question paying close attention in the solve efficiency of this complex combination and optimization problem.In fact,transportation cost control problems in logistics systems were dependent on logistics resources intregration efficient utilization.Therefore,it is difficult to meet the needs of cost reduction solely relying on the study of algorithms.In view of these facts,this paper has solved VRP by integration o self-support logistics and third-party logistics on the basis of existing research of VRP.Main research work and contributions are shown as follows.(1)To solve the vehicle scheduling and routing optimization problem which only have pickup demand.According to the feature of low demand and decentralized distribution customer,a concentrated treatment mode was used in the transport activity,and due to the limited of environmental factors,cannot ensure every path is feasible,transport vehicle needs to find alternative routes.An approach base-on feasibility of the path and concentrated treatment mode is developed for recycling path optimization,and an improved ACO algorithm by improving the coding mode and possible selection(ACO-nso)is proposed.The effectiveness of the algorithm is proven with an example and the applicable scope of the problem is discussed.The ACO-nso also promotes the research of path optimization and other combinatorial optimization problems.(2)To solve the vehicle scheduling and routing optimization problem which only have delivery demand.A novel partial collaborative transportation scheduling strategy is proposed based on two special kinds of transportation nodes which have integrated the self-support vehicle and 3PL vehicle resources.Under this transportation strategy,transportation graph of self-support vehicles and third-party vehicle s is a “Hamiltonian path + Hamiltonian cycle”.Then depending on the transport mode of each kind of transportation nodes,a modified Ant Colony Optimization with negative selection operation(ACO-nso)with varying dimension matrix encoding and modified transition probability operation method has been presented.Finally,simulative results demonstrate that the proposed approach is practical and efficient.(3)To solve the vehicle scheduling and routing optimization problem which both has delivery and pickup demand.Then,depending on the characteristics of delivery and pickup transportation networks,the PCT strategy and ACO-nso algorithm have further improved to solve the problems.The proposed methodology is tested on five illustrative cases with different characteristics of distribution and large-scale computational experiments based on Solomon test cases are presented to demonstrate the superiority and evaluate algorithm performance of our revised ACO-nso.Finally,simulative results demonstrate that the proposed approach is practical and efficient.The research results of this thesis can apply manufacturing enterprises which have own self-support logistics or E-commerce enterprise Self-built logistics systems.The third-party logistics not only increase capacity,it also deeply integrates in self-support logistics resources.The beneficial decision-making references to the resources utilization of validity and the reduction of logistics cost are proposed.