Dynamic revenue management in a passenger rail network under price and fleet management decisions

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company's historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company.