  Price differentiation is a common strategy in many markets. In this paper, we
study a static multiproduct price optimization problem with demand given by a
discrete mixed multinomial logit model. By considering a mixed logit model that
includes individual-specific variables in the utility specification, our
pricing problem reflects well the discrete choice models used in practice. To
solve this pricing problem, we design an efficient iterative optimization
algorithm that asymptotically converges to the optimal solution. To this end, a
linear optimization (LO) problem is formulated, based on the trust-region
approach, to find a "good" feasible solution and approximate the problem from
below. Another LO problem is designed using piecewise linear relaxations to
approximate the optimization problem from above. Then, we develop a new
branching method to tighten the optimality gap. The effectiveness of our
algorithm is demonstrated on a parking services pricing case, and a benchmark
against solvers and existing contributions in the literature are performed.
