A practical approach to revenue management in passenger train services: A case study of the Indonesian railways Argo Parahyangan

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.