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Marco Pranzo

Researcher at University of Siena

Publications -  89
Citations -  3571

Marco Pranzo is an academic researcher from University of Siena. The author has contributed to research in topics: Train & Job shop scheduling. The author has an hindex of 26, co-authored 85 publications receiving 3146 citations. Previous affiliations of Marco Pranzo include Delft University of Technology & Roma Tre University.

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A branch and bound algorithm for scheduling trains in a railway network

TL;DR: A branch and bound algorithm which includes implication rules enabling to speed up the computation of a train scheduling problem faced by railway infrastructure managers during real-time traffic control is developed.
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A tabu search algorithm for rerouting trains during rail operations

TL;DR: A number of algorithmic improvements implemented in the real-time traffic management system ROMA (Railway traffic Optimization by Means of Alternative graphs), achieved by incorporating effective rescheduling algorithms and local rerouting strategies in a tabu search scheme are described.
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Reordering and Local Rerouting Strategies to Manage Train Traffic in Real Time

TL;DR: The implementation of a real-time traffic management system, called ROMA (Railway traffic Optimization by Means of Alternative graphs), to support controllers in the everyday task of managing disturbances, making use of a branch-and-bound algorithm for sequencing train movements, while a local search algorithm is developed for rerouting optimization purposes.
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Conflict Resolution and Train Speed Coordination for Solving Real-Time Timetable Perturbations

TL;DR: An automated dispatching system provides better solutions in terms of delay minimization when compared to dispatching rules that can be adopted by a human traffic controller.
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Bi-objective conflict detection and resolution in railway traffic management

TL;DR: In this article, the authors considered the bi-objective problem of minimizing train delays and missed connections in order to provide a set of feasible non-dominated schedules to support this decisional process.