Abstract We are concerned with computing bid prices in network revenue management using approximate linear programming. It is well-known that affine value function approximations yield which not sensitive to remaining capacity. The analytic reduction compact programs allows the efficient computation of such prices. On other hand, capacity-dependent can be obtained separable piecewise approximat...

We investigate dynamic policies for allocating scarce inventory to stochastic demand for multiple fare classes, in a network environment so as to maximize total expected revenues. Typical applications include sequential reservations for an airline network, hotel, or car rental service. We propose and analyze a new algorithm based on approximate dynamic programming, both theoretically and comput...

The emergence of budget airlines, the advent of new sales channels, the disappearance of traditional fare-class fences, and other on-going changes in the airline industry pose serious challenges to traditional revenue management models, which make some rigid and unrealistic assumptions. One of these is that passengers who do not get the fare they want book and travel on other airlines or do not...

The practice of Revenue Management has received widespread acceptance in the international hospitality industry yet a lack of best practice in terms of organizational integration persists. This paper follows the notion that revenue management is first and foremost a human activity, dependent on knowledge exchange and concerted decision within revenue management teams. One critical attribute of ...

R management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations for the capacity allocation problem in revenue management using the maximin and the minimax regret criteria...