Robust Optimization with Recovery: Application to Shortest Paths and Airline Scheduling

In this exploratory paper we consider a robust approach to decisional problems subject to uncertain data in which we have an additional knowledge on the strategy (algorithm) used to react to an unforeseen event or recover from a disruption. This is a typical situation in scheduling problems where the decision maker has no a priori knowledge on the probabilistic distribution of such events but he only knows rough information on the event, such as its impact on the schedule. We discuss a general framework to address this situation and its links with other existing methods, we present an illustrative example on the Shortest Path Problem with Interval Data (SPPID) and we discuss a more general application to airline scheduling with recovery.

Presented at:
Swiss Transport Research Conference, Monte Verità, Switzerland, September 12-14

 Record created 2008-02-15, last modified 2018-01-28

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