ACM Computing Surveys (CSUR)
Domain-dependent distributed models for railway scheduling
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Reordering and Local Rerouting Strategies to Manage Train Traffic in Real Time
Transportation Science
A new class of greedy heuristics for job shop scheduling problems
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
Computers and Operations Research
A set packing inspired method for real-time junction train routing
Computers and Operations Research
On-line reschedule optimization for passenger railways in case of emergencies
Computers and Operations Research
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This paper deals with the development of decision support systems for traffic management of large and busy railway networks in case of severe disturbances. Railway operators typically structure the control of complicated networks into the coordinated control of several local dispatching areas. A dispatcher takes rescheduling decisions on the trains running on its local area while a coordinator addresses global issues that may arise between areas. While several advanced train dispatching models and algorithms have been proposed to support the dispatchers' task, the coordination problem did not receive much attention in the literature on train scheduling. This paper presents new heuristic algorithms for both local dispatching and coordination and compares centralized and distributed procedures to support the task of dispatchers and coordinators. We adopt dispatching procedures driven by optimization algorithms and based on local or global information and decisions. Computational experiments on a Dutch railway network, actually controlled by ten dispatchers, assess the performance of the centralized and distributed procedures. Various traffic disturbances, including entrance delays and blocked tracks, are analyzed on various time horizons of traffic prediction. Results show that the new heuristics clearly improve the global performance of the network with respect to the state of the art.