Optimization of seat allocation with fixed prices: An application of railway revenue management in China

Abstract

China Railway Corporation (CRC) has been paid more attention to passenger transportation revenue, with its increase proportion in transportation revenue. Due to the price regulation, the only way CRC can improve ticket sale profit is to find a best seat allocation scheme. This study focuses on the optimization of railway revenue management problem in China with consideration of i) customer behaviors including their arrival and purchase preferences, ii) a specific ticket booking mechanism called "seat-based control". To evaluate the performance of seat-based control, we build a Discrete-Time Markov Chain model to describe the ticket reservation process and then design a genetic algorithm to find approximate solutions. The performance of proposed method is tested in two experiments with two other benchmarks. Finally, we apply it to practical data of the Nanning-Guangzhou high-speed railway line.

An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion

“… Gopalakrishnan and Rangaraj (2010) consider train capacity management for long-distance passenger services. Recent studies on quantity-based RM system evaluate the effect of passengers’ choice behavior, arrival process, stochastic demand and control strategy on the revenue ( Wang et al, 2016 , Yuan and Nie, 2020 ). Further, Yan et al (2020) introduce capacity elasticity at high-speed rail (HSR) operations through a flexible train composition.…”

“…In contrast, the nested method defines seat availability based on class hierarchy, i.e., higher class subsumes lower class seats. Further, based on the type of control, the actual seat assignment is done at the time of ticket booking ( Yuan and Nie, 2020 , Yan et al, 2020 ) or before entering the station, i.e., in real-time ( Yazdani et al, 2019 ).…”

“…Fig. 5 , adapted from Yuan and Nie (2020) , illustrates a simple example of partitioned booking limit control (PBLC) trivially equivalent to PPLC for six seats and five different stations (A, B, C, D, E) along a train route. Based on passenger demand and ticket pricing, a total of eight groups (dotted squares) are formed that maximize revenue and eventually generate 11 different tickets (booking IDs).…”

“…The travel demand is assumed to be deterministic and easily estimated by aggregating the previous year’s data on final seat allocations and waitlisted passengers. On the other hand, the operational requirement of joint seat regulation in railways is easily assured by PPLC provided capacity constraints are met ( Ciancimino et al, 1999 , Yuan and Nie, 2020 ). Since a train runs through multiple sections along the route, the joint seat regulation ensures that passengers must be provided with the same seat at all sections of the itinerary.…”

An Optimization Method of Multiclass Price Railway Passenger Transport Ticket Allocation under High Passenger Demand

“…Due to its complexity, demand forecasting occupies most of the resources of the revenue management system [3], but the theoretical research on it has not received the same attention [3][4][5][6]. Demand data censoring is the most complex problem, and ignoring the effect of demand censoring will reduce the accuracy of forecasting [7,8].…”