Non-Cooperative Competition Among Revenue Maximizing Service Providers with Demand Learning
Document Type
Article
Publication Date
9-16-2009
Keywords
revenue management, pricing, demand learning, differential games, Kalman filters
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.ejor.2007.12.041
Abstract
This paper recognizes that in many decision environments in which revenue optimization is attempted, an actual demand curve and its parameters are generally unobservable. Herein, we describe the dynamics of demand as a continuous time differential equation based on an evolutionary game theory perspective. We then observe realized sales data to obtain estimates of parameters that govern the evolution of demand; these are refined on a discrete time scale. The resulting model takes the form of a differential variational inequality. We present an algorithm based on a gap function for the differential variational inequality and report its numerical performance for an example revenue optimization problem.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
European Journal of Operational Research, v. 197, issue 3, p. 981-996
Scholar Commons Citation
Kwon, Changhyun; Friesz, Terry L.; Mookherjee, Reetabrata; Yao, Tao; and Feng, Baichun, "Non-Cooperative Competition Among Revenue Maximizing Service Providers with Demand Learning" (2009). Industrial and Management Systems Engineering Faculty Publications. 19.
https://digitalcommons.usf.edu/egs_facpub/19
