Abstract
The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method’s application in the ferry transportation business and discuss its advantages.
Highlights
Revenue Management of perishable assets has been thoroughly studied in the airline setting (Littlewood 2005; Weatherford and Bodily 1992; McGill and van Ryzin 1999; Belobaba 2016; Weatherford 2016)
Different methods have been proposed to optimise revenue given this setup, including leg-based optimisation and origin-todestination optimisation (Belobaba 2016). All these methods rely on the use of historical demand data and the ability to describe that data as statistical distributions of demand by fare class (Weatherford 2016)
We propose a novel method based on the similarity between the departure to optimise and historical departures that handles deviations in demand automatically
Summary
Revenue Management of perishable assets has been thoroughly studied in the airline setting (Littlewood 2005; Weatherford and Bodily 1992; McGill and van Ryzin 1999; Belobaba 2016; Weatherford 2016). Each fare class should have a distinct and unique price interval, such that price overlap between fare classes is minimised. These fare classes have been fenced, to prevent buy-down from corporate travel customers. Different methods have been proposed to optimise revenue given this setup, including leg-based optimisation and origin-todestination optimisation (Belobaba 2016). All these methods rely on the use of historical demand data and the ability to describe that data as statistical distributions of demand by fare class (Weatherford 2016). Inaccurate demand forecasts can have significant impact on revenue (Weatherford and Belobaba 2002), both under and over forecasting of demand can be beneficial in certain circumstances (Mukhopadhyay et al 2007) leaving the decision to the revenue manager
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