Shunting Strategy for Placing-In and Taking-Out Wagons on Branch-Shaped Freight Operation Network with Simulation Method

Abstract

Because of China’s vast territory, large population, and huge demand for bulk materials, the railway transportation mode has always received considerable attention. For long-haul transportation, railway transportation can provide a scheduled-based transport plan, all-weather transport service, and cheaper carry instead of other transport modes. The study described in this paper attempts to develop a simulation platform for optimizing the placing-in and taking-out wagons system on the branch-shaped freight operation network (PTWS-BSFON). The operation process of PTWS is thoroughly analyzed from three aspects of decoupling-coupling wagons subsystem, placing-in local wagons subsystem, and taking-out local wagons subsystem. And then the simulation platform encompassing two typically shunting modes of PTWS are developed by Arena software. Under scenarios of PTWS-SO and PTWS-SSMS, the hierarchical structure of the shunting strategies is, respectively, outlined in the simulation platform. Finally, the shunting strategies based on the simulation platform are carried out by concrete examples, which prove the rationality of the methodology in applying different strategies and enhancing the performances of the PTWS-BSFON.

The Sequence Optimization of the Railway Tree-Shaped Special Line's Shunting for Taking-out and Placing-in of Wagons

“…Guo et al [2] established an optimization model for wagons' placing-in and taking-out, and designed its heuristic algorithm. Li et al [3] set out to develop a simulation approach for evaluating and optimizing the placing-in and taking-out wagons system on the branch-shaped freight operation network (PTWS-BSFON) on the basis of economic considerations. Qi [4] constructed the optimization model of wagon placing-in and taking-out under the condition of the fluctuant travel time, then, the hybrid intelligent algorithm is presented based on stochastic simulation, neural network, and genetic theory.…”