Abstract
Rescheduling trains in dense railway systems to cope in real time with limited disturbances is a challenging problem with multiple conflicting objectives and various types of decisions. Based on the French railway system in the Paris region, this paper proposes an approach combining multi-objective optimization, to select rescheduling decisions, and macroscopic simulation, to compute the objectives associated to these decisions. Possible decisions include canceling or short-turning trains and skipping or adding stops. Three main objectives are optimized to propose multiple solutions to the decision makers: The recovery time, the quality of service for passengers and the number of decisions. Two greedy heuristics are presented whose results on actual data are compared with a full enumeration method. The multi-objective feature of the approach is also analyzed. The implementation and successful validation in real life of a decision-support tool, that is now implemented, is discussed.
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