A Variable Neighborhood Search Heuristic for Rolling Stock Rescheduling

Rowan Hoogervorst, Twan Dollevoet, Gábor Maróti and Dennis Huisman ()

No EI2019-34, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: We present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, we then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three neighborhoods that can be used for rolling stock rescheduling, which respectively focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips. These neighborhoods are used for both a Variable Neighborhood Descent local search procedure and for perturbing the current solution in order to escape from local optima. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions in a reasonable amount of time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling.

Keywords: Disruption Management; Rolling Stock Rescheduling; Variable Neighborhood Search (search for similar items in EconPapers)
Pages: 30
Date: 2019-12-01
New Economics Papers: this item is included in nep-cmp
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