Computing virtual nesting controls for network revenue management under customer choice behavior

被引:60
作者
van Ryzin, Garrett [1 ]
Vulcano, Gustavo [2 ]
机构
[1] Columbia Univ, Grad Sch Business, New York, NY 10027 USA
[2] New York Univ, Stern Sch Business, New York, NY 10012 USA
关键词
choice behavior; revenue management; network capacity control; stochastic approximation; stochastic gradients;
D O I
10.1287/msom.1070.0210
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
We consider a revenue management, network capacity control problem in a setting where heterogeneous customers choose among the various products offered by a firm ( e. g., different flight times, fare classes, and/or routings). Customers may therefore substitute if their preferred products are not offered. These individual customer choice decisions are modeled as a very general stochastic sequence of customers, each of whom has an ordered list of preferences. Minimal assumptions are made about the statistical properties of this demand sequence. We assume that the firm controls the availability of products using a virtual nesting control strategy and would like to optimize the protection levels for its virtual classes accounting for the ( potentially quite complex) choice behavior of its customers. We formulate a continuous demand and capacity approximation for this problem, which allows for the partial acceptance of requests for products. The model admits an efficient calculation of the sample path gradient of the network revenue function. This gradient is then used to construct a stochastic steepest ascent algorithm. We show the algorithm converges in probability to a stationary point of the expected revenue function under mild conditions. The algorithm is relatively efficient even on large network problems, and in our simulation experiments it produces significant revenue increases relative to traditional virtual nesting methods. On a large-scale, real-world airline example using choice behavior models fit to actual booking data, the method produced an estimated 10% improvement in revenue relative to the controls used by the airline. The examples also provide interesting insights into how protection levels should be adjusted to account for choice behavior. Overall, the results indicate that choice behavior has a significant impact on both capacity control decisions and revenue performance and that our method is a viable approach for addressing the problem.
引用
收藏
页码:448 / 467
页数:20
相关论文
共 30 条
[1]  
Algers S., 2001, International Journal of Services Technology and Management, V2, P28, DOI 10.1504/IJSTM.2001.001589
[2]  
Andersson S.-E., 1998, International Transactions in Operational Research, V5, P471, DOI 10.1111/j.1475-3995.1998.tb00130.x
[4]  
Belobaba P.P., 1987, Air travel demand and airline seat inventory management
[5]   Comparing decision rules that incorporate customer diversion in perishable asset revenue management situations [J].
Belobaba, PP ;
Weatherford, LR .
DECISION SCIENCES, 1996, 27 (02) :343-363
[6]  
BELOBABA PP, 1999, P 1999 AGIFORS RES Y
[7]  
Benveniste A, 1990, Adaptive algorithms and stochastic approximations
[8]  
Bertsekas D., 1999, NONLINEAR PROGRAMMIN
[9]   Simulation-based booking limits for airline revenue management [J].
Bertsimas, D ;
de Boer, S .
OPERATIONS RESEARCH, 2005, 53 (01) :90-106
[10]  
BOYD EA, 2004, J REVENUE PRICING MA, V3, P171, DOI DOI 10.1057/PALGRAVE.RPM.5170104