A quasi-robust optimization approach for crew rescheduling

This paper studies the real-time crew rescheduling problem in case of large-scale disruptions. One of the greatest challenges of real-time disruption management is the unknown duration of the disruption. In this paper we present a novel approach for crew rescheduling where we deal with this uncertainty by considering several scenarios for the duration of the disruption. The rescheduling problem is similar to a two-stage optimization problem. In the first stage, at the start of the disruption, we reschedule the plan based on the optimistic scenario (i.e., assuming the shortest possible duration of the disruption), while taking into account the possibility that another scenario will be realized. We require a prescribed number of the rescheduled crew duties (a sequential list of tasks which have to be performed by a single crew member) to be recoverable. The true duration of the disruption is revealed in the second stage. By the recoverability of the duties, we expect that the first stage solution can easily be turned into a schedule that is feasible for the realized scenario. We demonstrate the effectiveness of our approach by an application in real-time railway crew rescheduling. The ideas of this paper generalize to certain vehicle rescheduling and manufacturing problems where timetabled tasks which have a fixed start and end location are to be carried out by a given number of servers. We test our approach on a number of instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. The numerical experiments show that the approach indeed finds schedules which are easier to adjust if it turns out that another scenario than the optimistic one is realized for the duration of the disruption.

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