Research On Operation Optimization Of Railway Container Central Station Based On Customer Classification

Highly convenient and high-capacity container transportation is an important method to realize globalization of trade and integration of various modes of transportation.In the context of the "Belt and Road" policy and the market-oriented reform of the railway,and the railway sector also adapt to the buyer-oriented buyer market.At present,the main bottleneck of railway transportation's efficiency has changed from transportation time to transportation node operation time.As the link between the cargo owner and the railway operator,the railway container central station is not only the window for the railway operator to connect with customers,but also the important node of the railway freight network.The operation organization process and operation efficiency in the station directly affect the overall efficiency and service quality of the railwaynet work.In this context,this paper takes the railway container central station as the research object considering the customer's demand characteristics,uses CPNTOOL simulation and improved genetic algorithm to optimize the equipment resource allocation and scheduling problems.The main research contents are as follows:Firstly,the paper combine with the characteristics of customer needs and the characteristics of the organization of the central station,use CPNTOOL simulation to obtain the optimal equipment configuration scheme for the container central station.Based on the customer relationship management theory,this paper divides the railway freight owners into three types:agreement customers,ordinary customers and expedited customers.Combined with the station operation organization process,this paper establishes a hierarchical timed colored Petri net model to simulate different equipment configuration schemes,and obtains optimal configuration.The solution establish a good foundation for subsequent resource scheduling optimization.Secondly,on the basis of customer demand analysis,learning from mixed flow shop scheduling optimization idea,the paper abstract the the central station equipment resource scheduling problem into two progressive problems:one is the known equipment quantity,solving the machine selection problem under the customer separation mode.Another is the known number of equipments and optimal machine selection,solving scheduling problem under the customer mixed mode.For the first problem,the equipment resource scheduling optimization model is established separately without considering the influence of customers,and the paper designed a niche genetic to solve the the optimal machine selection for each task in the case of customers.'For question two,combine all customer types to resolve tasks that need to occupy the same resource at the same time.The paper construct a nonlinear moxied integer programming model and design a greedy genetic algorithm to reschedule the all tasks in the customer mixed mode.In the greedy strategy,the paper consider the customer attributes and the acceptable moving time window.The example shows that the optimization algorithm is superior to the common genetic algorithm in solving quality,and the solving idea in this paper has some reference value in optimizing the mixed complexity problem.