In this paper, we address the problem of
dynamic pricing to optimize the revenue coming from the sales of a limited
inventory in a finite time-horizon. A priori, the demand is assumed to be unknown.
The seller must learn on the fly. We first deal with the simplest case,
involving only one class of product for sale. Furthermore the general situation
is considered with a finite number of product classes for sale. In particular,
a case in point is the sale of tickets for events related to culture and
leisure; in this case, typically the tickets are sold months before the event,
thus, uncertainty over actual demand levels is a very a common occurrence. We
propose a heuristic strategy of adaptive dynamic pricing, based on experience
gained from the past, taking into account, for each time period, the available
inventory, the time remaining to reach the horizon, and the profit made in
previous periods. In the computational simulations performed, the demand is
updated dynamically based on the prices being offered, as well as on the
remaining time and inventory. The simulations show a significant profit over
the fixed-price strategy, confirming the practical usefulness of the proposed
strategy. We develop a tool allowing us to test different dynamic pricing
strategies designed to fit market conditions and seller's objectives, which
will facilitate data analysis and decision-making in the face of the problem of
Cite this paper
Vázquez-Gallo, M. , Estévez, M. and Egido, S. (2014) Active Learning and Dynamic Pricing Policies. American Journal of Operations Research
, 90-100. doi: 10.4236/ajor.2014.42009
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