A Fast Heuristic Algorithm for the Train Unit Assignment Problem

In this paper we study a railway optimization problem known as the Train Unit Assignment Problem. A train unit consists of a self-contained train with an engine and a set of wagons with passenger seats. Given a set of timetabled train trips, each with a required number of passenger seats, and a set of train units, each with a given number of available seats, the problem calls for the best assignment of the train units to the trips, possibly combining more than one train unit for a given trip, that fulfills the seat requests. We propose a heuristic algorithm based on the computation of a lower bound obtained by solving an Integer Linear Programming model that gives the optimal solution in a "peak period" of the day. The performance of the heuristic algorithm is computationally evaluated on real-world instances provided by a regional Italian Train Operator. The results are compared with those of existing methods from the literature, showing that the new method is able to obtain solutions of good quality in much shorter computing times

Robust Train Routing and Online Re-scheduling

Train Routing is a problem that arises in the early phase of the passenger railway planning process, usually several months before operating the trains. The main goal is to assign each train a stopping platform and the corresponding arrival/departure paths through a railway station. It is also called Train Platforming when referring to the platform assignment task. Railway stations often represent bottlenecks and train delays can easily disrupt the routing schedule. Thereby railway stations are responsible for a large part of the delay propagation in the whole network. In this research we present different models to compute robust routing schedules and we study their power in an online context together with different re-scheduling strategies. We also design a simulation framework and use it to evaluate and compare the effectiveness of the proposed robust models and re-scheduling algorithms using real-world data from Rete Ferroviaria Italiana, the main Italian Railway Infrastructure Manager

A railway timetable rescheduling approach for handling large scale disruptions

On a daily basis, relatively large disruptions require infrastructure managers and railway operators to reschedule their railway timetables together with their rolling stock and crew schedules. This research focuses on timetable rescheduling for passenger trains at a macroscopic level in a railway network. An integer programming model is formulated for solving the timetable rescheduling problem, which minimizes the number of cancelled and delayed trains while adhering to infrastructure and rolling stock capacity constraints. The possibility of rerouting trains in order to reduce the number of cancelled and delayed trains is also considered. In addition, all stages of the disruption management process (from the start of the disruption to the time the normal situation is restored) are taken into account. Computational tests of the described model on a heavily used part of the Dutch railway network show that we are able to find optimal solutions in short computation times. This makes the approach applicable for use in practice

Whole-body isometric force/torque measurements for functional assessment in neuro-rehabilitation: platform design, development and verification

<p>Abstract</p> <p>Background</p> <p>One of the main scientific and technological challenges of rehabilitation bioengineering is the development of innovative methodologies, based on the use of appropriate technological devices, for an objective assessment of patients undergoing a rehabilitation treatment. Such tools should be as fast and cheap to use as clinical scales, which are currently the daily instruments most widely used in the routine clinical practice.</p> <p>Methods</p> <p>A human-centered approach was used in the design and development of a mechanical structure equipped with eight force/torque sensors that record quantitative data during the initiation of a predefined set of Activities of Daily Living (ADL) tasks, in isometric conditions.</p> <p>Results</p> <p>Preliminary results validated the appropriateness, acceptability and functionality of the proposed platform, that has become now a tool used for clinical research in three clinical centres.</p> <p>Conclusion</p> <p>This paper presented the design and development of an innovative platform for whole-body force and torque measurements on human subjects. The platform has been designed to perform accurate quantitative measurements in isometric conditions with the specific aim to address the needs for functional assessment tests of patients undergoing a rehabilitation treatment as a consequence of a stroke.</p> <p>The versatility of the system also enlightens several other interesting possible areas of application for therapy in neurorehabilitation, for research in basic neuroscience, and more.</p

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 stoc

Instances for the Recoverable Robust Two-Level Network Design Problem

We provide the instances used in the paper "The Recoverable Robust Two-Level Network Design Problem", by E. Alvarez-Miranda, I. Ljubic, S. Raghavan and P. Toth, accepted for publication in the INFORMS J. on Computing, 2014 (http://dx.doi.org/10.1287/ijoc.2014.0606). This repository contains both the instances used in the paper as well as the results obtained by the proposed algorithm

CARD15 genotyping in inflammatory bowel disease patients by multiplex pyrosequencing

[No abstract available