Optimizing the Number of Express Freight Trains on a High-speed Railway Corridor by the Departure Period

Abstract

In China, the transportation capacity of high-speed railways is gradually sufficient to provide services for high-value express freight besides meeting passenger demands. However, similar as the passengers, express freight demands fluctuate and show clear peak and trough periods daily. Therefore, optimizing running numbers of express freight trains on high-speed railway by periods is quite necessary to guarantee the revenues of railway industry and to meet the various requirements of all consignees simultaneously. First, a space-period-pattern three-dimensional network with virtual arcs is built to describe the departure period selection and the stopping or skip-stopping operations. Second, by constructing the arcs' impedance function, the user equilibrium principle is introduced to optimize the express freight flow distribution for each pattern in each period. To elaborate the comprehensive goal of balancing the relationship between profits and the flow distribution, a bi-level programming model is established. The upper model addresses the railway industry's maximum profits, and the lower model addresses the minimum and similar impedance values of the final express freight flow distribution. Finally, through the use of a hybrid algorithm that combines the heuristic genetic algorithm with the Frank-Wolfe algorithm, an experimental case of 10 stations on the Beijing-Xi'an high-speed railway corridor is used to validate the study. The results show that the express freight volume is reasonably distributed to each kind of virtual arc, the impedance value of each pattern is minimized and almost equalized, and the profits of railway industry are maximized by optimizing the number and departure time of trains by each pattern in each period on the basis of meeting all the express freight demands. INDEX TERMS High-speed railway corridor, express freight train, period-congestion, stopping or skipstopping operating pattern, bi-level programming model.

Pricing of the Bus-Truck Co-Delivery Mode of Last Mile Delivery Considering Social Welfare Maximization

“…C CV is determined by the per kilometer fuel cost, c f ; the travel distance of the trucks at the two ends (d OF m and d ED ); and the number of deliveries, as shown in Equation (14). The cost of bus delivery, C CB , is the freight paid to the bus operator, as shown in Equation (15).…”

“…The research on passenger and cargo integrated transportation was first proposed by Trentini and Mahléné in 2010 [11], and then it was explored in air, sea [12], railway [13,14], and intercity transportation [15], but rarely in urban express delivery. Sampaio et al [16] analyzed a case of passenger and cargo integrated transportation in urban express delivery.…”

A Bi-Level Multi-Objective Optimization Model for Micro-Circulation Road Networks in the Open Block Area Considering Traffic Pollution and Intersection Delays

“…GA is inspired by the biological evolution process and it is widely used to generate high-quality solutions for optimization and search problems [44]. In addition, GA is characterized by global superiority and dramatic convergence [45]. Compared with other heuristic algorithms, GA has three main advantages: (1) The operation object of GA is a group of feasible solutions rather than a single solution.…”

“…Finally, we designed a genetic algorithm to solve the upper-level programming model and embedded the Frank-Wolfe algorithm to obtain the traffic demand distribution of the lower-level programming model [45]. GA is the main body of the whole algorithm, which deals with the nonlinear and non-convex properties of bi-level programming.…”

Review of freight high speed railway (HSR)

“…For this reason, plans, considerations, simulations, optimizations [2,54], predictions and real solutions for the use of HSR (High Speed Railway) for high speed freight transport [20,35,51,52,60] have been developed. Including comparative analysis of the use of air transport and HSR [47,56], as well as the punctuality of express parcels transport by various means of transport [32] and the selection of optimal locations of consolidation centers to reduce GHG [9], mainly on China example. Works related to Freight HSR also include the development of tracking of transported goods [57], intelligent management systems [6], monitoring and calculations related to the rolling stock [3,16] and issues related to the aerodynamics of HSR vehicles [50].…”