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Research by Itir Karaesmen and Michael Ball
Airlines want to sell their seats to the right customers at the right time
for the right price. The more fare classes or products they have, the more
difficult it is to optimize the revenue for each product. That is, airlines have
turned to complex mathematical models for revenue management.
Hotels, cruise lines and car rental companies all use similarly complicated
models for revenue management. These models require information about how many
people are going to want those airline seats in the future—they need to forecast
demand for their product into the future. Unfortunately, forecasting is
notoriously difficult and demand estimates are inaccurate. The state of the
economy, seasonal variations in travel demand and even the competitive actions
of other airlines make finding reliable information even more difficult.
The inherent challenges in creating accurate demand forecasts has been a key
factor limiting the realistic application of revenue management within new
industries, and can cause problems for new products within these industries,
such as new airline routes or new properties in a hotel chain.
Side-stepping the problem of demand forecasting can provide a robust solution
to these difficulties, according to research by Michael Ball, Orkand Corporation
Professor of Management Science, and Itir Karaesmen, assistant professor of
management science, with doctoral students Yingjie Lan and Huina Gao, who have
developed models that use minimal demand information for revenue management.
Rather than try to create a better forecasting system, the authors worked
with the least amount of information to provide robust methods that match demand
with supply effectively. They created a model that maximizes revenue under the
worst-case scenario for an airline without extensive demand information. In
fact, the model does not need any information about demand or the arrival
process beyond a simple upper and lower bounds that represent the aggregate
demand; this type of information is easier to obtain from expert judgment when
there is no historical sales data. The approach relies on competitive analysis
of online algorithms, and focuses on “regret” which measures the loss in
performance due to lack of information: it compares what the seller can do with
limited information versus what he could have done if he had perfect information
on-demand.
The policies derived from the model are robust, since they guarantee a
certain performance level under all possible demand scenarios. And the average
revenues from these policies are comparable to other well-known procedures, even
though they use less information.
It’s counterintuitive to think that you could have less information and yet
get a result that is as reliable as a result gained from a plethora of data.
“Using less information, we can still get very good results that will allow
airlines to make very good decisions,” says Karaesmen. “In some cases you will
never have good data. Can you actually make decisions without good data? This
paper shows that you can, and in a way that is much faster than existing
methods.”
New and smaller airlines, such as Virgin America, or airlines that are
opening new flights, might find this model useful, because it allows for
reliable revenue management without requiring a stream of pre-existing data to
fuel demand-forecasting models.
“Forecasting is a challenge because airlines and hotels only have data on
what they have sold. They don’t know what they could have sold,” says Karaesmen.
“This is a very safe way of starting a business, until you get to a point where
you have lots of data and can use more sophisticated methods.”
But it could also be used as a quality control method by airlines that have
made significant investments in demand forecasting. Ball and Karaesmen’s model
could run in the background as a type of quality control; if the actual
performance differed greatly from the results given by the model, managers could
identify errors before they became problematic.
This work builds on prior research by Ball (with Maurice Queyranne of
University of British Columbia), who used competitive analysis of online
algorithms to produce policies that did not rely on any demand information.
Future papers will add a layer of complexity to the problem by considering the
problem of overbooking and the effects of competition on this model.
“Revenue Management with Limited Demand Information” will be published in
Management Science. This research was funded in part by a grant from the
National Science Foundation. For more information about this research, contact
ikaraes@rhsmith.umd.edu or
mball@rhsmith.umd.edu.
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