Rolling Stock Planning Research Articles

Integrating stop planning, timetabling and rolling stock planning on high-speed railway lines: A multi-objective optimization approach

Designing railway operational planning is a complex and challenging task to cope with fluctuating passenger demand and satisfy actual operational requirements. Focusing on high-speed railway (HSR) lines, this paper proposes an integrated modeling framework for phase-oriented railway operational planning. The framework simultaneously optimizes the stop planning, train timetable problem (TTP) and rolling stock planning (RSP). A multi-objective optimization model, with macro passenger demand, two train speed levels, and overtaking operations taken into account, is formulated to minimize the total train running distance, train dwell time, and rolling stock resources. The constraints involve the train operation zones, train operation process, passenger demand, rolling stock circulation conditions, and actual operational requirements. The proposed model is addressed using a framework based on the ε-constraint method, from which an approximate Pareto-optimal solution set is obtained. A case study on the Beijing-Shanghai HSR corridor demonstrates the effectiveness of the proposed approach and reveals some trade-offs between the proposed objectives.

Risk assessment in railway rolling stock planning

Rolling stock planning is one of the steps in the traffic planning process considered from the railway undertaking's point of view. It is directly related to the efficiency of rolling stock utilisation, which should be ensured at the highest possible level in the case of rail transport. The planning work stage is subject to certain risks (threats and opportunities), which, if they materialise, will impact it. It, therefore, makes sense to carry out analyses that can anticipate specific events in good time and introduce appropriate countermeasures in advance. This article aims to conduct a risk assessment process concerning rolling stock planning. It was assumed that the considerations were carried out based on the M_o_R (Management of Risk) methodology. Based on this methodology, risk identifica-tion and risk analysis (estimation of risk impact) were carried out. Risk assessment was carried out using the Monte Carlo simulation method. The work identified sixteen risks that represent threats. The principle of risk description was used to identify risks. It requires indicating for each risk the reason for its occurrence and the effect it may have. As a part of risk estimation, variables were selected to assess each risk's impact on the objectives of the stage. Publicly available statistical data were used to define the variables. The variables were expressed in monetary units. The work identified five variables describing impact, which were assigned to the individual risks. As a trian-gular probability distribution was used for the variability of impact description, the variable's minimum, most likely, and maximum value was identified. A risk assessment was carried out for only two impact description varia-bles (for those variables used to describe the impact of the most significant number of risks). For each variable, statistical parameters were indicated and analysed. The resulting value of the variable describing the impact was then read out for each percentile, and the expected value of the risk was calculated. A detailed risk assessment was made for the lower, middle and upper quartiles. A histogram of the incidence of each variable value was presented, and an assessment was made.

Weekly rolling stock planning in Chinese high-speed rail networks

In high-speed rail networks, train units are scheduled to periodically meet all maintenance requirements while at the same time continuing to serve all scheduled passenger trips. Motivated by the trip demand variances on the days of every week in China, this paper studies a weekly rolling stock planning (W-RSP) problem that aims to optimize the rotation plan for the train units on each day of a week, so as to minimize their operating cost, including any (un)coupling costs and maintenance costs. We model the W-RSP on a newly developed rotation network by adopting particular nodes and arcs to address the (un)coupling operations of train units, and then propose an integer linear programming formulation for the problem. To solve this formulation, we develop a customized branch-and-price algorithm, which relies on a reduced linear programming relaxation for computing the lower bound, embeds a diving algorithm for computing the upper bound, and integrates advanced branching rules for effective explorations of the solution space. Computational results validate the effectiveness and efficiency of the proposed solution algorithm, which is able to solve large instances with up to 5034 trips to near-optimality.

Maintenance scheduling within rolling stock planning in railway operations under uncertain maintenance durations

Optimizing railway and rolling-stock operations, reducing costs and improving service reliability are goals that should be pursued and adapted to the current and future customer demand, while complying with maintenance and safety requirements. This paper presents an integer linear programming mathematical model, which schedules maintenance tasks for a given period, within the normal rolling-stock service operations. This model is applied to a case study and a reliability analysis is conducted, assessing the impact of uncertainty associated with maintenance durations in the optimal value for the objective function, as well as in the feasibility of such a maintenance schedule.

An Effective Peak Period Heuristic for Railway Rolling Stock Planning

In this work we tackle a real-world application of railway rolling stock planning, known as the train unit assignment problem (TUAP), arising for a regional train operator in the North of Italy. Gi...

An integrated rolling stock planning model for the Copenhagen suburban passenger railway

A central issue for operators of passenger railways is providing sufficient number of seats for passengers while at the same time minimising operating costs. This is the task of rolling stock planning. Due to the large number of practical, railway specific requirements that a rolling stock plan has to take into account, rolling stock plans are often constructed in a step-by-step manner, taking some requirements into consideration in each step. This may make it difficult in the final step to produce a plan that is feasible with regard to all of the requirements and at the same time economically attractive.This paper proposes an integrated rolling stock planning model that simultaneously takes into account all practical requirements for rolling stock planning at DSB S-tog, the suburban passenger train operator of the City of Copenhagen. The model is then used to improve existing rolling stock plans using a hill climbing heuristic.Experiments show that the heuristic used in the integrated rolling stock planning model is able to produce feasible solutions within minutes of computation time starting from infeasible rolling stock plans. Furthermore, the heuristic is able to improve the economic attractiveness of typical rolling stock plans with an average of 2%.

S141016 異なる車両編成タイプを有する鉄道車両運用計画問題に対するアントコロニー最適化法に基づく解法([S141]生産システムの新展開(基礎・理論))

S141016 異なる車両編成タイプを有する鉄道車両運用計画問題に対するアントコロニー最適化法に基づく解法([S141]生産システムの新展開(基礎・理論))

Railway Rolling Stock Planning: Robustness Against Large Disruptions

In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large mixed-integer linear programming (MILP) model. We first use Benders decomposition to determine optimal solutions for the LP-relaxation of this model. Then we use the cuts that were generated by the Benders decomposition for computing heuristic robust solutions for the two-stage optimization model. We call our method Benders heuristic. We evaluate our approach on the real-life rolling stock-planning problem of Netherlands Railways, the main operator of passenger trains in the Netherlands. The computational results show that, thanks to Benders decomposition, the LP-relaxation of the two-stage optimization problem can be solved in a short time for a representative number of disruption scenarios. In addition, they demonstrate that the robust rolling stock circulation computed heuristically has total costs that are close to the LP lower bounds. Finally, we discuss the practical effectiveness of the robust rolling stock circulation: When a large number of disruption scenarios were applied to these robust circulations and to the nonrobust optimal circulations, the former appeared to be much more easily recoverable than the latter.

A Comprehensive Approach for Rolling Stock Planning: Combining Train Performance Simulation and Life-Cycle Cost Analysis

Rolling stock has a limited life span during which it can provide efficient service. Faced with today's budget constraints and economic slowdown, all railroad or transit operators will eventually face the following questions: What is the most suitable equipment available to replace the existing equipment for the particular service? What is the most economic way of procuring the required fleet? To answer those questions, various railroad agencies and transit operators used diversified measures such as capacity demanded by the ridership forecast, performance afforded by the proposed equipment, or the operating and capital cost associated with certain rolling stock configurations. However, comprehensive analyses based on all three factors mentioned above are rare, even though it is agreed that all those factors affect railroad services simultaneously and they all should be considered in the rolling stock planning process. To fill the gap, this paper describes a comprehensive approach to select long-term rolling stock for commuter services. This approach not only considers the performance of the proposed equipment but also evaluates the life cycle costs of the proposed fleet configuration. A case study of rolling stock planning for a commuter rail service is included to demonstrate the practical application of the suggested approach. Combining the train performance simulation (TPS) and life cycle cost (LCC) analysis, the proposed approach derives an optimal balance between service costs and service quality. Moreover, by incorporating the timing of the new equipment purchases into the life cycle cost, this approach further maximizes the return on capital investment for transit agencies.

Combinatorial and robust optimisation models and algorithms for railway applications

The following is a summary of the author’s Ph.D. thesis supervised by Alberto Caprara and Paolo Toth and defended on April 16, 2009 at the Universita di Bologna. The thesis is written in English and is available from the author upon request. Railway systems represent a challenging area for operations research, especially when highly-complex and data-intensive applications, such as large-scale transportation networks, are at stake. One of the main issues concerns imperfect information. The classic notion of Robust Optimisation, as a way to represent and handle mathematically systems with not precisely known data, did not prove to be successfully applicable in the railway setting. For this reason a new paradigm has been defined recently in Liebchen et al. (2007): Recoverable robustness. Here we present our research on recoverable robust optimisation models for two important railway problems: Train platforming and Rolling stock planning.

S1401-1-1 複数コロニーを用いたアントコロニー最適化法による旅客鉄道の車両運用計画([S1401-1]生産システムの新展開(基礎・理論))

S1401-1-1 複数コロニーを用いたアントコロニー最適化法による旅客鉄道の車両運用計画([S1401-1]生産システムの新展開(基礎・理論))

アントコロニー最適化法による旅客鉄道の車両運用計画(機械力学,計測,自動制御)

アントコロニー最適化法による旅客鉄道の車両運用計画(機械力学,計測,自動制御)

E25 旅客鉄道の車両運用計画問題に対するメタヒューリスティック・アプローチ : ACOとGAの性能比較(E2 ロボット・制御(人間機械系と情報システム))

E25 旅客鉄道の車両運用計画問題に対するメタヒューリスティック・アプローチ : ACOとGAの性能比較(E2 ロボット・制御(人間機械系と情報システム))