2015
DOI: 10.1057/rpm.2015.42
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Simulating the flavors of revenue management for airlines

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Cited by 8 publications
(5 citation statements)
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References 11 publications
(15 reference statements)
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“…Non-cruise literature such as airlines or industries involved in a nested network can be beneficial. For instance, Doreswamy et al's (2015) proposed forecasting model moved away from leg-based (voyage) to full origindestination (full itinerary) control can help to increase 1% to 6% of airlines revenue.…”
Section: **Insert Table 1**mentioning
confidence: 99%
“…Non-cruise literature such as airlines or industries involved in a nested network can be beneficial. For instance, Doreswamy et al's (2015) proposed forecasting model moved away from leg-based (voyage) to full origindestination (full itinerary) control can help to increase 1% to 6% of airlines revenue.…”
Section: **Insert Table 1**mentioning
confidence: 99%
“…On the other hand, Doreswamy et al, (2015) showcase a successful application of a simulator to test the implementation of RM systems. The researchers calibrated the simulator using real airline controls as a reference and reported the benefits that the use of these tools present.…”
Section: Chairs Comparison To Other Simulatorsmentioning
confidence: 99%
“…The associated benefits that a well-applied RM system reports to airlines justify the need for a simulation tool capable of replicating scenarios in controlled environments in efficient and affordable ways. Counting with the appropriate tools to propose and test new policies or adjust RM systems already in use by airlines can help RM practitioners learn (Cleophas, 2012) and improve their understanding (Doreswamy et al, 2015) of the problems faced in competitive environments. Such a tool could be essential in the industry's current state, with macroeconomic events rapidly shifting competitive contexts.…”
Section: Introductionmentioning
confidence: 99%
“…The paper at hand follows these contributions in establishing a simulation-based framework to generate outlier observations. Doreswamy et al (2015) employ simulation as a tool to analyse the effects of different revenue management techniques for different airlines, when switching from leg-based controls to network controls. Cleophas et al (2009) focus on an approach to evaluating the quality of RM forecasts in the airline setting, both in terms of revenue and common forecast error measurements.…”
Section: Rm Forecasts and Forecast Evaluationmentioning
confidence: 99%