Dynamic railcar connection planning in classification yards

Li, H.; Jin, Mingzhou; Song, Rui; He, Shiwei; Song, Jinglu

doi:10.1179/1942787515y.0000000010

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…Hauser and Maue (13) and Jha et al (14) designed heuristic optimization methods for solving the block-totrack assignment problem by assuming the yard capacity to be fixed. Li et al used a heuristic algorithm based on the harmony search strategy to solve the problem of rail car connection between inbound trains and outbound trains (15). Optimization methods often are difficult to implement in practice because of their complexity and the lack of flexibility to incorporate changing operational conditions.…”

Section: Optimization Methodsmentioning

confidence: 99%

Macrolevel Classification Yard Capacity Modeling

Zhang

Jin

et al. 2017

Transportation Research Record

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Classification yards play a significant role in railroad freight transportation and are often considered bottlenecks for railroad networks. Based on a generic yard simulation model, the model in the presented study fits the Bureau of Public Roads function, which is widely used in highway capacity to represent the volume–dwell time relationship. The proposed analytical model incorporates major features of rail yards, such as the number and capacity of tracks in each area, the number of engines and humps, the humping speed, and the assemble rate. The model is validated by historical data from 16 classification yards of Class I railroads in the United States. The results show that the proposed model can generate precise capacity data of rail yard, as well as the dwell time of rail cars in yards. The dwell time increases sharply when the volume is greater than the capacity of a rail yard. The identified relationship may help a railroad analyze its network at the macro level and therefore improve the systemwide capacity and efficiency.

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Section: Optimization Methodsmentioning

confidence: 99%

Macrolevel Classification Yard Capacity Modeling

Zhang

Jin

et al. 2017

Transportation Research Record

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“…The development of different simulation tools (see e.g., Kavicka and Klima, ; Lin and Cheng, ; Marinov and Viegas, ) enhanced the modeling of marshaling yards providing a wide range of evaluations for online and offline analysis regarding the behavior and performance of marshaling procedures. In recent years, mathematical programming with artificial intelligence and expert systems have been used to create different computer‐aided decision support systems for operational optimization of marshaling yards (see e.g., Adlbrecht et al., ; Li et al., ; Haahr and Lusby, ).…”

Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Multistage Train Classification at Strategic Planning Level

Belošević

Ivic

2017

Computer aided Civil Eng

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Considerations on influences and interrelations between structural design and functional planning are imperative for raising the cost and operational effectiveness of marshaling yards in the context of managing wagonload services. This article focuses on multistage train classification, which rearranges a set of wagons from inbound trains to create a set of multigroup outbound trains in a proper form. Previous papers on the subject of multistage train classification were limited to considerations on the optimal classification schedule addressing the number of sorting steps as a primary objective. In contrast, this article proposes a model for overall optimization of the classification schedule and sidings layout, simultaneously addressing the number of sorting steps and the total number of wagon movements in the objective function. The model is created on a binary integer programming formulation. A Variable Neighborhood Search (VNS) heuristic is developed due to the computational complexity of the problem. Numerical experiments validate the formulated model and demonstrate the efficiency of the VNS meta‐heuristic approach.

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“…1 The article also claimed that the freight volume in the United States is projected to grow by at least 80% in the next 20 years which will have significant knock-on effects on delays. It is a well known fact that freight cars, in a rail network, spend most of their time in terminals or classification yards (Li et al, 2014). This is due to the fact that the same facility has to be used for consolidation and classification operations for a variety of vehicles carrying different types of freight.…”

Section: Introductionmentioning

confidence: 99%

The congested multicommodity network design problem

Paraskevopoulos

Gürel

Bektaş

2016

Transportation Research Part E: Logistics and Transportation Re

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This paper studies a version of the fixed-charge multicommodity network design problem where in addition to the traditional costs of flow and design, congestion at nodes is explicitly considered. The problem is initially modeled as a nonlinear integer programming formulation and two solution approaches are proposed: (i) a reformulation of the problem as a mixed integer second order cone program to optimally solve the problem for small to medium scale problem instances, and (ii) an evolutionary algorithm using elements of iterated local search and scatter search to provide upper bounds. Extensive computational results on new benchmark problem instances and on real case data, are presented.

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Dynamic railcar connection planning in classification yards

Cited by 6 publications

References 33 publications

Macrolevel Classification Yard Capacity Modeling

Macrolevel Classification Yard Capacity Modeling

Variable Neighborhood Search for Multistage Train Classification at Strategic Planning Level

The congested multicommodity network design problem

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