Distributed algorithms for the computation of noncooperative equilibria
Automatica (Journal of IFAC)
Differential games in economics and management science
Differential games in economics and management science
A Randomized Linear Programming Method for Computing Network Bid Prices
Transportation Science
Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Inventory Competition Under Dynamic Consumer Choice
Operations Research
Centralized and Competitive Inventory Models with Demand Substitution
Operations Research
Revenue Management in a Dynamic Network Environment
Transportation Science
Revenue Management Games: Horizontal and Vertical Competition
Management Science
Manufacturing & Service Operations Management
Models of the Spiral-Down Effect in Revenue Management
Operations Research
Approximations of Nash equilibria
Mathematical Programming: Series A and B
Generalized Nash equilibrium problems and Newton methods
Mathematical Programming: Series A and B
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We consider capacity management games between airlines who transport passengers over a joint airline network. Passengers are likely to purchase alternative tickets of the same class from competing airlines if they do not get tickets from their preferred airlines. We propose a Nash and a generalized Nash game model to address the competitive network revenue management problem. These two models are based on well-known deterministic linear programming and probabilistic nonlinear programming approximations for the non-competitive network capacity management problem. We prove the existence of a Nash equilibrium for both games and investigate the uniqueness of a Nash equilibrium for the Nash game. We provide some further uniqueness and comparative statics analysis when the network is reduced to a single-leg flight structure with two products. The comparative statics analysis reveals some useful insights on how Nash equilibrium booking limits change monotonically in the prices of products. Our numerical results indicate that airlines can generate higher and more stable revenues from a booking scheme that is based on the combination of the partitioned booking-limit policy and the generalized Nash game model. The results also show that this booking scheme is robust irrespective of which booking scheme the competitor takes.