  In this paper, we solve the multiple product price optimization problem under
interval uncertainties of the price sensitivity parameters in the demand
function. The objective of the price optimization problem is to maximize the
overall revenue of the firm where the decision variables are the prices of the
products supplied by the firm. We propose an approach that yields optimal
solutions under different variations of the estimated price sensitivity
parameters. We adopt a robust optimization approach by building a data-driven
uncertainty set for the parameters, and then construct a deterministic
counterpart for the robust optimization model. The numerical results show that
two objectives are fulfilled: the method reflects the uncertainty embedded in
parameter estimations, and also an interval is obtained for optimal prices. We
also conducted a simulation study to which we compared the results of our
approach. The comparisons show that although robust optimization is deemed to
be conservative, the results of the proposed approach show little loss compared
to those from the simulation.
