Abstract
This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.
Highlights
In recent years, high-speed railway (HSR) in China has developed rapidly, but operating revenue varies greatly between HSR lines
With the upgrading of Ticket Selling and Booking System of China’s Railway, 12306 network booking systems can carry out real-time dynamic seat reuse, and these conventional ticket organization strategies have been weakened for HSR
Railway departments pay more attention to adjusting seats flexibly according to ticket sales results, but ‘preferentially selling long-distance tickets to long-distance passengers’ is still the basic principle of seat management. e reform of ticket price policy and seat management has created conditions for China’s HSR to implement revenue management (RM) strategy
Summary
High-speed railway (HSR) in China has developed rapidly, but operating revenue varies greatly between HSR lines. Multiple stops for a single HSR service are the most significant difference between these two transportation modes, which increases the complexity of joint pricing and seat allocation for HSR. Most existing studies on the joint optimization of pricing and seat allocation in the rail market only considered a small network or a few train services, which leaves a large gap between the numerical experiments and real-world problems. Is study attempts to make contributions to the existing research on large-scale joint optimization of pricing and seat allocation for HSR by (1) establishing a joint optimization model of multistage pricing and seat allocation, (2) reformulating the optimization model as a bilevel multifollower programming model in which the upper-level model solves the large-scale static seat allocation problem for multiple products in the whole booking horizon and the lower optimizes the small-scale multistage dynamic pricing decisions for each product in different decision periods, (3) improving the ticket price constraints considered by existing studies and considering price time constraints in each period and price space constraints among products, and (4) designing an efficient solution method based on divide-andconquer strategy for large-scale problems.
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