Abstract:
We proposed a practical approach to incorporating demand function in pricing optimization and
its corresponding capacity allocation problem. Based on the willingness-to-pay approach, demand
function was estimated from choice-based conjoint (CBC) data using a combination of hierarchical
Bayes estimation, randomized first choice simulation, and cubic spline interpolation. The
approach was implemented in the largest intercity passenger train service in Indonesia, assuming
two customer segments and four fare classes. Solution to the mixed-integer nonlinear pricing
optimization problem was obtained using enumeration. Subsequently, the expected marginal seat
revenue heuristic was used in the seat inventory allocation problem.
