3,935 research outputs found
Operations research in passenger railway transportation
In this paper, we give an overview of state-of-the-art OperationsResearch models and techniques used in passenger railwaytransportation. For each planning phase (strategic, tactical andoperational), we describe the planning problems arising there anddiscuss some models and algorithms to solve them. We do not onlyconsider classical, well-known topics such as timetabling, rollingstock scheduling and crew scheduling, but we also discuss somerecently developed topics as shunting and reliability oftimetables.Finally, we focus on several practical aspects for each of theseproblems at the largest Dutch railway operator, NS Reizigers.passenger railway transportation;operation research;planning problems
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
An approximate dynamic programming approach for designing train timetables
Traditional approaches to solving the train timetabling problemāthe optimal allocation of when each train arrives and departs each stationāhave relied on Mixed-Integer Programming (MIP) approaches. We propose an alternative formulation for this problem based on the modeling and algorithmic framework of approximate dynamic programming. We present a Q-learning algorithm in order to tractably solve the high-dimensional problem. We compare the performance of several variants of this approach, including discretizing the state and the action spaces, and continuous function approximation with global basis functions. We demonstrate the algorithms on two railway system cases, one minimizing energy consumption subject to punctuality constraints, and one maximizing capacity subject to safety constraints. We demonstrate that the ADP algorithm converges rapidly to an optimal solution, and that the number of iterations required increases linearly in the size of the rail system, in contrast with MIP approaches whose computation time grows exponentially. We also show that an additional benefit to the ADP approach is the intuition gained from visualizing the Q-factor functions, which graphically capture the intuitive tradeoffs between efficiency and constraints in both examples
Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms
This paper provides an overview of the research in railway scheduling and dispatching. A distinction is made between tactical scheduling, operational scheduling and re-scheduling. Tactical scheduling refers to master scheduling, whereas operational scheduling concerns scheduling at a later stage. Re-scheduling focuses on the re-planning of an existing timetable when deviations from it have occurred. 48 approaches published between 1973 and 2005 have been reviewed according to a framework that classifies them with respect to problem type, solution mechanism, and type of evaluation. 26 of the approaches support the representation of a railway network rather than a railway line, but the majority has been experimentally evaluated for traffic on a line. 94 % of the approaches have been subject to some kind of experimental evaluation, while approximately 4 % have been implemented. The solutions proposed vary from myopic, priority-based algorithms, to traditional operations research techniques and the application of agent technology.This paper provides an overview of the research in railway scheduling and dispatching. A distinction is made between tactical scheduling, operational scheduling and re-scheduling. Tactical scheduling refers to master scheduling, whereas operational scheduling concerns scheduling at a later stage. Re-scheduling focuses on the re-planning of an existing timetable when deviations from it have occurred. 48 approaches published between 1973 and 2005 have been reviewed according to a framework that classifies them with respect to problem type, solution mechanism, and type of evaluation. 26 of the approaches support the representation of a railway network rather than a railway line, but the majority has been experimentally evaluated for traffic on a line. 94 % of the approaches have been subject to some kind of experimental evaluation, while approximately 4 % have been implemented. The solutions proposed vary from myopic, priority-based algorithms, to traditional operations research techniques and the application of agent technology
Stochastic Improvement of Cyclic Railway Timetables
Real-time railway operations are subject to stochastic disturbances. However, a railway timetable is a deterministic plan. Thus a timetable should be designed in such a way that it can cope with the stochastic disturbances as well as possible. For that purpose, a timetable usually contains time supplements in several process times and buffer times between pairs of consecutive trains. This paper describes a Stochastic Optimization Model that can be used to allocate the time supplements and the buffer times in a given timetable in such a way that the timetable becomes maximally robust against stochastic disturbances. The Stochastic Optimization Model was tested on several instances of NS Reizigers, the main operator of passenger trains in the Netherlands. Moreover, a timetable that was computed by the model was operated in practice in a timetable experiment on the so-called Ć¢ā¬ÅZaanlijnĆ¢ā¬. The results show that the average delays of trains can often be reduced significantly by applying relatively small modifications to a given timetable.Railway Timetabling;Stochastic Optimization;Robustness
An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
The single-track railway train timetabling problem (TTP) is an important and
complex problem. This article proposes an integrated Monte Carlo Tree Search
(MCTS) computing framework that combines heuristic methods, unsupervised
learning methods, and supervised learning methods for solving TTP in discrete
action spaces. This article first describes the mathematical model and
simulation system dynamics of TTP, analyzes the characteristics of the solution
from the perspective of MCTS, and proposes some heuristic methods to improve
MCTS. This article considers these methods as planners in the proposed
framework. Secondly, this article utilizes deep convolutional neural networks
to approximate the value of nodes and further applies them to the MCTS search
process, referred to as learners. The experiment shows that the proposed
heuristic MCTS method is beneficial for solving TTP; The algorithm framework
that integrates planners and learners can improve the data efficiency of
solving TTP; The proposed method provides a new paradigm for solving TTP
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