Shunting of Passenger Train Units in a Railway Station

In this paper we introduce the problem of shunting passenger trainunits in a railway station. Shunting occurs whenever train units aretemporarily not necessary to operate a given timetable. We discussseveral aspects of this problem and focus on two subproblems. Wepropose mathematical models for these subproblems together with asolution method based on column generation. Furthermore, a newefficient and speedy solution technique for pricing problems in columngeneration algorithms is introduced. Finally, we present computationalresults based on real life instances from Netherlands Railways.logistics;column generation;railway optimization;real world application

Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport

For the enormous capital investment and high operation expense of locomotives, the locomotive management/assignment and fuel consumption are two of the most important areas for railway industry, especially in freight train transportation. Several algorithms have been developed for the Locomotive Assignment Problem (LAP), including exact mathematics models, approximate dynamic programming and heuristics. These previously published optimization algorithms suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive operation, e.g., maintenance/shop constraints or consist busting avoidance. Furthermore, there are rarely research works for the reduction of total train energy consumption on the locomotive assignment level. The thesis is organized around our three main contributions. Firstly we propose a “consist travel plan” based LAP optimization model, which covers all the required meaningful constraints and which can efficiently be solved using large scale optimization techniques, namely column generation (CG) decomposition. Our key contribution is that our LAP model can evaluate the occurrence of consist busting using the number of consist travel plans, and allows locomotive status transformation in flow conservation constraints. In addition, a new column generation acceleration architecture is developed, that allows the subproblem, i.e., column generator to create multiple columns in each iteration, that each is an optimal solution for a reduced sub-network. This new CG architecture reduces computational time greatly comparing to our original LAP model. For train fuel consumption, we derive, linearize and integrate a train fuel consumption model into our LAP model. In addition, we establish a conflict-free pre-process for time windows for train rescheduling without touching train-meet time and position. The new LAP-fuel consumption model works fine for the optimization of the train energy exhaustion on the locomotive assignment level. For the optimization models above, the numerical results are conducted on the railway network infrastructure of Canada Pacific Railway (CPR), with up to 1,750 trains and 9 types of locomotives over a two-week time period in the entire CPR railway network

Signal synthesis by means of evolutionary algorithms

In this article, we investigate a procedure for generating signals with genetic algorithms. Signals are obtained from elementary patterns characterized by different degrees of freedom. These patterns are repeated and combined in order to reach specific signal shapes. The whole signal parametrization has to be determined by solving a difficult inverse problem of high dimensionality and strong multimodality. This can be carried out using evolutionary algorithms with the aim of finding all pattern configurations in the signal. The different signal synthesis schemes are evaluated, tested and applied to the generation of particular railway driving profiles

Train-scheduling optimization model for railway networks with multiplatform stations

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version

Integer programming based solution approaches for the train dispatching problem

Railroads face the challenge of competing with the trucking industry in a fastpaced environment. In this respect, they are working toward running freight trains on schedule and reducing travel times. The planned train schedules consist of departure and arrival times at main stations on the rail network. A detailed timetable, on the other hand, consists of the departure and arrival times of each train in each track section of its route. The train dispatching problem aims to determine detailed timetables over a rail network in order to minimize deviations from the planned schedule. We provide a new integer programming formulation for this problem based on a spacetime network; we propose heuristic algorithms to solve it and present computational results of these algorithms. Our approach includes some realistic constraints that have not been previously considered as well as all the assumptions and practical issues considered by the earlier works

Optimization models of rail transportation under the financial crisis

This paper proposes an analysis of the most used models to optimize the rail transportation. Are presented a series of optimization models of labor efficiency in this sector, but also elements that gives the information on the competitiveness of this mode of transport.railway, railway optimization, optimization models for railway

Assessment of the worthwhileness of efficient driving in railway systems with high-receptivity power supplies

Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency