Optimized shunting with mixed-usage tracks

We consider the planning of railway freight classification at hump yards, where the problem involves the formation of departing freight train blocks from arriving trains subject to scheduling and capacity constraints. The hump yard layout considered consists of arrival tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform and possibly non-sufficient length at a classification yard, and departure tracks of sufficient length. To increase yard capacity, freight cars arriving early can be stored temporarily on specific mixed-usage tracks. The entire hump yard planning process is covered in this paper, and heuristics for arrival and departure track assignment, as well as hump scheduling, have been included to provide the neccessary input data. However, the central problem considered is the classification track allocation problem. This problem has previously been modeled using direct mixed integer programming models, but this approach did not yield lower bounds of sufficient quality to prove optimality. Later attempts focused on a column generation approach based on branch-and-price that could solve problem instances of industrial size. Building upon the column generation approach we introduce a direct arc-based integer programming model, where the arcs are precedence relations between blocks on the same classification track. Further, the most promising models are adapted for rolling-horizon planning. We evaluate the methods on historical data from the Hallsberg shunting yard in Sweden. The results show that the new arc-based model performs as well as the column generation approach. It returns an optimal schedule within the execution time limit for all instances but from one, and executes as fast as the column generation approach. Further, the short execution times of the column generation approach and the arc-indexed model make them suitable for rolling-horizon planning, while the direct mixed integer program proved to be too slow for this. Extended analysis of the results shows that mixing was only required if the maximum number of concurrent trains on the classification yard exceeds 29 (there are 32 available tracks), and that after this point the number of extra car roll-ins increases heavily

Analytical Models in Rail Transportation: An Annotated Bibliography

Not AvailableThis research has been supported, in part, by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)

Cost modelling-based route applicability analysis of United Kingdom passenger railway decarbonization options

The UK government plans to phase out pure diesel trains by 2040 and fully decarbonize railways by 2050. Hydrogen fuel cell (HFC) trains, electrified trains using pantographs (Electrified Trains), and battery electric multiple unit (BEMU) trains are considered the main solutions for decarbonizing railways. However, the range of these decarbonization options’ line upgrade cost advantages is unclear. This paper analyzes the upgrade costs of three types of trains on different lines by constructing a cost model and using particle swarm optimization (PSO), including operating costs and fixed investment costs. For the case of decarbonization of the London St. Pancras to Leicester line, the electrified train option is more cost-effective than the other two options under the condition that the service period is 30 years. Then the traffic density range in which three new energy trains have cost advantages on different line lengths is calculated. For route distances under 100 km and with a traffic density of less than 52 trips/day, BEMU trains have the lowest average cost, while electrified trains are the most cost-effective in other ranges. For route distances over 100 km, the average cost of HFC trains is lower than that of electrified trains at traffic densities below about 45 trips/day. In addition, if hydrogen prices fall by 26 %, the cost advantage range of HFC trains will increase to 70 trips per day. For route distances under 100 km, BEMU trains still maintain their advantages in terms of lower traffic density

Train Unit Scheduling Optimization with Station Level Resolution

The train unit scheduling optimization (TUSO) problem aims at seeking a conflict-free operational plan for a set of train units to serve all trips defined in a fixed timetable with minimum operational costs. TUSO is addressed at two levels: the network level and the station level. The network level focuses on determining the serving sequence of trips for each train unit, where the stations are simplified as single points. The station level deals with the issues left in a network-level solution with detailed infrastructure restored. Prior to this research, TUSO at the network level, specific on the UK railway operating system, has been tackled as a multi-commodity network flow problem. Whereas train unit flows are balanced and optimised over the service network, potential operational conflicts due to layouts in individual train station have been ignored. This research mainly concerns resolving such operational conflicts at the station level. However, this research has also made contributions in improving the network flow model. This research follows the two-phase approach to tackle TUSO at these two levels. TUSO is first solved at the network level in Phase I, where two solvers have been developed, namely RS-Opt and SLIM. Given a solution from the network level, two operational aspects are left undetermined: coupling order issues and linkage feasibility. To finalize these two aspects, an adaptive approach expanding Phase I to Phase II is proposed. Phase II takes a further step of station-level resolution and attempts to complete a fully operable schedule. The logistics of coupling/decoupling activities and tentative linkages are determined in detail to prevent conflicts where possible, particularly focusing on developing an operable schedule without conflicts of coupling order or crossing linkages in train stations. If the unresolvable station-level conflicts still exist at Phase II, the process loops back to Phase I with added constraints to avoid the identified conflicts. Through these two phases, a global optimal solution that is also operable considering station-level layouts will be secured. Moreover, the observation on the network-level experimental results from the existing RS-Opt and SLIM has inspired the research on improving the network flow model from the perspective of considering additional terms in the objective function such as the slack time and the number of cars. It is extended as a new methodology to evaluate the effectiveness of alternative objective function designs

A branch-and-price approach for trip sequence planning of high-speed train units

In high-speed railway operations, a trip sequence plan is made once the timetable is determined, and serves as a reference in the subsequent operations of train units scheduling. In light of the maintenance requirements of train units and periodicity characteristics of trip sequences, we introduce a trip sequence graph to describe the train units’ movement and coupling/splitting in a railway network. Based on the trip sequence graph, two integer linear programming models are then formulated, namely a path-based model and an arc-based model. Integrated with the characteristics of the trip sequence graph, a customized branch-and-price algorithm is developed to solve the path-based model. The two models are applied to the high-speed railway network in eastern China, and through numerical experiments, the effectiveness and applicability of the models are discussed