Study On The Optimization Of Railway Empty Container Disposition Under The Uncertain Environment

For the development of railway transport of goods, the need to break from the demandsto improve service quality and quality of transport in two ways. Especially to develop roads,waterways and pipeline transportation in recent years, resources are part of the railwaytransport of goods to snatch a fact, and the main reason is that other modes of transport toprovide more convenient and cheaper services for shippers. To address this situation, railwaybegan to focus on the development of railway container transport in recent years, containertransport has been used as an advanced mode of transport in China and has entered a leapstage of development, because of easy handling, simplified packaging of goods, cargodamage rate is small and easy to realize the advantages of door to door transport.Transport of goods to achieve based on the premise of mobile containers, but emptycontainers flowing state of imbalance has emerged, which the main resource is our specialenvironment and economic development environment determines of the difference in thedistribution and supply of container flows, so imbalance of container flows can only beresolved by transporting empty containers. However, an empty container repositioning doesnot produce the product during transport, which is not only a waste of railway transportcapacity while extending the empty container idle time for rail transport. In order to minimizethe impact of both, you need to study the allocation of empty containers rational decisiontheory and methods to reduce the cost of transporting empty containers are inevitablygenerated.The core issue of the allocation of empty containers is time, location and quantity oftransporting empty containers, which have been made great achievements by manyresearchers in this regard. However, with the economic development and the continuousimprovement of customer needs, there is a big change in adjusted operating railway plan, andmany uncertainties for making the allocation of empty containers, particularly complexenvironment and transport container transport operations. Mainly in the number of emptycontainers transported box shipped to randomness and fuzziness of time.Empty container repositioning uncertainty is dominated by empty container transporttime, changes in market supply and demand and the creating transportation organization,which affects the fundamental supply and demand factors. Throughout the empty containersduring transport, the train departure time, technical operating time, are subject to change intransit time and arrival time, but the paper uses the customer needs time to limit the scope ofthe simulation time variable; Meanwhile, train marshalling content, the proportion of emptycontainers, empty containers and boxes kinds of alternative ownership is a random variable affecting supply and demand changes. To solve this problem, this paper first studies theempty container transportation planning and fuzzy random chance opportunities for planning.Empty Container shipping is a classic problem and has been very mature theory. Butunloading containers under uncertainty conditioned transportation problems, the amount ofcontainer supply and demand between the station and the station is no longer balanced. Andsimply consider the transporting cost minimization has been unable to meet customer demandfor transport, empty containers must be added to customer service time limits, set a reasonabletime constraints connecting transport and rail transport capacity constraints, combined withempty boxes of random variables and time of fuzzy variables constraints to model. Therefore,this paper studied the transport uncertain environment based on minimizing the cost ofrailway transportation problem of empty containers.For general chance constrained by determining equivalence formula can be transformedinto a constraint to determine. But this transformation is restricted range of applications, wepropose the use of hybrid intelligent algorithm to solve the model with variables. The biggestadvantage of this hybrid intelligent algorithm is stochastic simulation and fuzzy simulationcan produce large amounts of input data, the capacity of the training sample is large, andrelies on the neural network weights update and detection of chromosomal genetic algorithm,making random constraints and fuzzy constraint no longer need to be converted into adetermined expression to solve. It expands the range of applications, especially to be able tofind the best chromosome as the optimal solution, avoiding the traditional algorithm operatingresults precocious shortcomings while providing a neural network error.Finally, Xi’an Railway is a case study examines the feasibility of the model, andproposed deficiencies and innovation thesis.