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Large-scale dynamic network pricing under customer choice behavior for railway companies
Hohberger, Simon
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Schön, Cornelia
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Dokumenttyp:
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Präsentation auf Konferenz
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Erscheinungsjahr:
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2018
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Veranstaltungstitel:
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OR2018 - International Conference on Operations Research
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Veranstaltungsort:
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Brussels, Belgium
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Veranstaltungsdatum:
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12.-14.09.2018
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > Service Operations Management (Schön 2014-)
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Fachgebiet:
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330 Wirtschaft
650 Management
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Freie Schlagwörter (Englisch):
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Dynamic Pricing , Revenue Management , Customer Choice , Railway Application
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Abstract:
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Railway Revenue Management (RRM) offers great opportunities to increase revenues in practice but it received only little attention in research during the past decades. Due to the special structure of railway networks, traditional RM-approaches that have been applied in other transportation industries must be critically assessed and adjusted to the industry specific setting. In our talk, we present a mathematical model formulation for multi-product, multi- resource railway revenue management for dynamic pricing that incorporates heterogeneous customer choice behavior and railway specific constraints.
We first give practical insights into the special business environment that railway companies are dealing with. This includes the motivation for the use of customer choice models. A train company typically offers multiple connections between two cities within a day (e.g. 37 between Frankfurt and Berlin for Deutsche Bahn) and therefore the demand for a specific train connection does not only depend on its own price but also on the price and quality of alternative connections. In addition, some practical business rules should be considered in a RRM-model, e.g. longer itineraries should be more expensive than shorter ones to avoid strategic customers. As a result of incorporating the characteristics mentioned above, the original mathematical problem formulation turns out be non-convex nonlinear with potentially many local optima, and is thus difficult to solve exactly. At the same time, problem instances in reality are typically large-scale and the allowable time to solve the problem in practice very limited. Therefore, we present heuristics that make it possible to solve large-scale problem instances approximately in reasonable time. Finally, based on real data from the German Railway Company Deutsche Bahn we show first results with respect to solution quality and time and discuss chances and limits that arise in practice with a choice- based RM approach.
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