Delay Management Problem: Complexity Results and Robust Algorithms
In this paper, we study the problem of planning a timetable for passenger trains considering that possible delays might occur due to unpredictable (but bounded) circumstances. Once arrival and departure events are scheduled, if the timetable cannot be respected since an external event has determined...
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Published in | Combinatorial Optimization and Applications Vol. 5165; pp. 458 - 468 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Germany
Springer Berlin / Heidelberg
2008
Springer Berlin Heidelberg |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3540850961 9783540850960 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-540-85097-7_43 |
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Abstract | In this paper, we study the problem of planning a timetable for passenger trains considering that possible delays might occur due to unpredictable (but bounded) circumstances. Once arrival and departure events are scheduled, if the timetable cannot be respected since an external event has determined a delay to a train, the so called delay management problem occurs. Delays might be managed in several ways and the usual objective function considered for such purpose is the minimization of the overall waiting time caused to passengers.
We analyze the interaction between timetable planning and delay management in terms of the recoverable robustness model, where a timetable is said to be robust if it is able to absorb small delays by possibly applying given recovery capabilities. The quality of a robust timetable is measured by the price of robustness that is the ratio between the cost of the robust timetable and that of a non-robust optimal timetable.
We consider the problem of designing robust timetables subject to bounded delays. We show that finding an optimal solution for this problem is NP-hard. Hence, we propose robust algorithms and evaluate their prices of robustness. Moreover, we show that such algorithms are optimal with respect to particular assumptions. |
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AbstractList | In this paper, we study the problem of planning a timetable for passenger trains considering that possible delays might occur due to unpredictable (but bounded) circumstances. Once arrival and departure events are scheduled, if the timetable cannot be respected since an external event has determined a delay to a train, the so called delay management problem occurs. Delays might be managed in several ways and the usual objective function considered for such purpose is the minimization of the overall waiting time caused to passengers.
We analyze the interaction between timetable planning and delay management in terms of the recoverable robustness model, where a timetable is said to be robust if it is able to absorb small delays by possibly applying given recovery capabilities. The quality of a robust timetable is measured by the price of robustness that is the ratio between the cost of the robust timetable and that of a non-robust optimal timetable.
We consider the problem of designing robust timetables subject to bounded delays. We show that finding an optimal solution for this problem is NP-hard. Hence, we propose robust algorithms and evaluate their prices of robustness. Moreover, we show that such algorithms are optimal with respect to particular assumptions. |
Author | Di Stefano, Gabriele Navarra, Alfredo D’Angelo, Gianlorenzo Frigioni, Daniele Cicerone, Serafino |
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Copyright | Springer-Verlag Berlin Heidelberg 2008 |
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Editor | Wang, Cao An Du, Ding-Zhu Yang, Boting |
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Notes | This work was partially supported by the Future and Emerging Technologies Unit of EC (IST priority - 6th FP), under contract no. FP6-021235-2 (project ARRIVAL). |
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Snippet | In this paper, we study the problem of planning a timetable for passenger trains considering that possible delays might occur due to unpredictable (but... |
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StartPage | 458 |
SubjectTerms | Disturbing Event Robust Algorithm Robust Optimization Slack Time Timetabling Problem |
Title | Delay Management Problem: Complexity Results and Robust Algorithms |
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