Simulation of planning strategies for track allocation at marshalling yards

Abstract

Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled at an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they're supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method.

The accumulation cost of relaxed fixed time accumulation mode

“…FIGURE 14 Benefit ratios corresponding to the optimal minimum numbers of wagons under different wagon flow intensities Figure 14 shows the relationship between the benefit ratios and optimal minimum number of wagons:…”

“…Marinov and Viegas [12,13] used SIMUL'8 to establish a simulation model for analysing and evaluating the operation of a flat-shunted yard. Bohlin et al [14] compared different track allocation strategies in the accumulation process of wagons through a simulation method, namely the 'first come first served' strategy and the departure time limit strategy, and found that the departure time limit strategy performed better. Khadilkar and Sinha [15] simulated operation processes such as arrival, breaking-up and marshalling of reorganization trains in the marshalling yard, and obtained the operation scheme with the shortest waiting time of wagons.…”

Quantifying the Influence of Volume Variability on Railway Hump Classification Yard Performance with AnyLogic Simulation

“…In reviewing and summarizing simulation tools used in the past and the current state-of-the-art in railroad terminal simulation models, the authors identified three major types of simulation tools: general coding languages, general-purpose simulation software, and custom commercial simulations (Tables 1–3). General coding languages (Table 1) are the most common tools, with researchers converting yard operation to a Monte Carlo process ( 31 , 32 ), mathematical functions ( 32 – 35 ), a queuing model ( 36 ), or a discrete-event list ( 37 , 38 ) featuring computerized simulation instead of manual calculation. Despite the flexibility and ease of building the model, common limitations of these general coding languages are a lack of detailed operating and geometric features and an inability to reflect the specific traffic flow movements of the yard operation.…”

Influence of traffic complexity on railway gravity hump classification yard performance and capacity

“…58,59 SIMUL8 was used for discrete-event simulation of the largest flat-switching yard in Portugal 60 while a MATLAB model simulated the Hallsberg hump classification yard in Sweden. 52 Hump classification yards have also been simulated in the general-purpose AnyLogic simulation environment. 18,30 CSX and Optym partnered to develop the web-based Hump Yard Simulation System (HYSS) now known as YardSYM and consisting of a discrete-event simulation model for analysis of hump classification yards.…”

“…18 Initial simulation models were based on computerized versions of the yard charts used in manual simulation. [52][53][54][55][56][57] Among these early efforts, Reinschmidt examined the influence of various infrastructure and resource parameters on hump yard capacity through singlevariable experiments and analysis. 29 This paper specifically examines the interaction between volume, number of blocks and yard dwell time.…”