Seat inventory control methods for Chinese passenger railways

Yun Bao , Jun Liu , Min-shu Ma , Ling-yun Meng

Journal of Central South University ›› 2014, Vol. 21 ›› Issue (4) : 1672 -1682.

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Journal of Central South University ›› 2014, Vol. 21 ›› Issue (4) :1672 -1682. DOI: 10.1007/s11771-014-2109-y
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Seat inventory control methods for Chinese passenger railways

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Abstract

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers’ historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Keywords

seat inventory control / Chinese passenger railway / revenue management / booking limits / bid-price

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Yun Bao, Jun Liu, Min-shu Ma, Ling-yun Meng. Seat inventory control methods for Chinese passenger railways. Journal of Central South University, 2014, 21(4): 1672-1682 DOI:10.1007/s11771-014-2109-y

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References

[1]

TalluriK T, Van RyzinG JThe theory and practice of revenue management [M], 2005, New York, Springer: 1-10

[2]

ChiangW C, ChenJ C H, XuX-jing. An overview of research on revenue management: Current issues and future research [J]. International Journal of Revenue Management, 2007, 1(1): 97-128

[3]

YouP S. An efficient computational approach for railway booking problems [J]. European Journal of Operation research, 2008, 185: 811-824

[4]

BelobabaP P. Air travel demand and airline seat inventory management [D]. Cambridge: Massachusettes Institute of Technology, 19878-9

[5]

CianciminoA, InzerilloG. A mathematical programming approach for the solution of the railway yield management problem [J]. Transportation Science, 1999, 33(2): 168-181

[6]

SibdariS, LinK, ChellappanS, et al. . Multiproduct revenue management: An empirical study of auto train at amtrak [J]. Journal of Revenue and Pricing Management, 2008, 7(2): 172-184

[7]

BharillR, RangarajR. Revenue management in railway operations: A study of the Rajdhani Express, indian railways [J]. Transportation Research Part A, 2008, 42: 1195-1207

[8]

EverittB SCluster analysis [M], 1993Third editionLondon, Halsted Press: 254-255

[9]

BezdekJ C. Some new indexes of clustering validity [J]. IEEE Trans on Systems, Man, and Cybernetics, 1998, 28(3): 301-315

[10]

LiY-b, ZhangN, LiC-bin. Support vector machine forecasting method improved by chaotic particle swarm optimization and its application [J]. Journal of Central South University Technology, 2009, 16: 478-481

[11]

GloverF, GloverR, LorenzoJ, McmillanC. The passenger mix problem in the scheduled airlines [J]. Interfaces, 1982, 12: 73-79

[12]

SmithB C, PennC W. Analysis of alternative origin-destination control strategies [C]. In Proceedings of the 28th Annual AGIFORS Symposium. New Seabury, MA, 1988123-144

[13]

BertsimasD, de BoerS. Simulation-based booking-limits for airline revenue management [J]. Operation Research, 2005, 53(1): 90-106

[14]

HigleJ L. Bid-price control with origin-destination demand: A stochastic programming approach [J]. Journal of Revenue and Pricing Management, 2006, 5(4): 291-304

[15]

VollingT, AkyolD E, WittekK, SpenglerT S. A two-stage bid-price control for make-to-order revenue management [J]. Computers & OR, 2012, 39(5): 1021-1032

[16]

WangX-b, WangF-huan. Dynamic network yield management [J]. Transportation Research Part B, 2007, 41: 410-425

[17]

ErtsimasD, PopescuI. Revenue management in a dynamic network environment [J]. Transportation Science, 2003, 37: 257-277

[18]

SimpsonR W. Using network flow techniques to find shadow prices for market and seat inventory control [R]. Technical Report Memorandum, M89-1, 1989, Cambridge, MA, Flight Transportation Laboratory, MIT

[19]

WilliamsonE LAirline network seat inventory control: Methodologies and revenue impacts [D], 1992, Cambridge, Massachusettes Institute of Technology

[20]

TalluriK T, Van RyzinG J. An analysis of bid-price controls for network revenue management [J]. Management Science, 1998, 44(11): 1577-1593

[21]

AdelmanD. Dynamic bid-prices in revenue management [J]. Operations Research, 2007, 55(4): 647-661

[22]

TopalogluH. Using lagrangian relaxation to compute capacity-dependent bid prices in network revenue management [J]. Operations Research, 2009, 57: 637-649

[23]

MeissnerJ, StraussA K. Network revenue management with inventory-sensitive bid prices and customer choice [J]. European Journal of operation research, 2012, 216(2): 459-468

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