Practice Oriented Algorithmic Disruption Management in Passenger Railways Defended on Thursday, 8 September 2016

This thesis addresses a question railway companies face on a daily basis, namely how to deal with a disruption? How can you handle a disruption such that the passenger service is upheld as much as possible? The current mathematical models for disruption management can not yet be applied in practice, because several important practical considerations are not taken into account. In this thesis several models are presented taking important practical details into account: (1) creating a macroscopic globally feasible solution for all three resource schedules, instead of focussing on one individual resource schedule. (2) Taking maintenance appointments required by certain rolling stock units into account while rescheduling. (3) Dead-heading trips to transfer rolling stock units from stations with a surplus of inventory to stations with a shortage of inventory. (4) Adjusted passenger demand, the passenger demand is not static, but depends on the capacity appointed to the previous trips. Finally, (5) checking whether a rolling stock circulation is feasible with respect to the available depot tracks (the shunting yard) within a station. We make use of different techniques to solve the models, for instance, mixed integer linear programming, column generation, constraint programming, and heuristic models are used in this thesis. The results demonstrate that these five practical considerations can be taken effectively into account in the disruption management models.


Disruption Management, Rolling Stock Rescheduling, White Spots, Maintenance Appointments, Dead-Heading Trips, Adjused Passenger Demand, Decision Support System, Passenger Oriented

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