Network revenue management is concerned with managing demand for products that require inventory from one or several resources by controlling product availability and/or prices in order to maximize expected revenues subject to the available resource capacities. One can tackle this problem by decomposing it into resource-level subproblems that can be solved efficiently, e.g. by dynamic programmi...

In this paper, we present new approximation methods for the network revenue management problem with customer choice behavior. Our methods are sampling-based and so we require only minimal assumptions regarding the underlying customer choice model. The starting point for our methods is a dynamic program that allows randomization. An attractive feature of this dynamic program is that the size of ...

Proof of Proposition 1. With a slight abuse of notation, let us denote by D d the set of multivariate stochastic processes such that the aggregate demand equals d. Accordingly, Problem (5) can be decomposed as follows: ρ(y) = max d∈P max D∈D d max z∈F R(z, D) − R(y, D) = max d∈P max z∈F max D∈D d R(z, D) + max D∈D d −R(y, D). Let ξ j be the realized sales under a booking policy z when the deman...

In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a sub...

We propose a new method to compute bid prices in network revenue management problems. The novel aspect of our method is that it naturally provides dynamic bid prices that depend on how much time is left until departure. We show that our method provides an upper bound on the optimal total expected revenue and this upper bound is tighter than the one provided by the widely known deterministic lin...

We consider a network revenue management problem with customer choice and exogenous prices. We study the performance of a class of certainty equivalent heuristic control policies. These heuristics periodically re-solve the deterministic Linear Program (DLP) that results when all future random variables are replaced by their average values and implement the solutions in a probabilistic manner. W...

This paper studies a supply chain coordinated by a revenue-sharing contract in fuzzy environment. Two kinds of fuzzy programming models are discussed: revenue-sharing contract with fuzzy linear demand model and fuzzy iso-price-elastic demand model. Supply chain coordination in both centralized decision setting and centralized decision setting are achieved by obtaining the optimal solutions in t...

The network choice revenue management problem models customers as choosing from an offer set, and the firm decides the best subset to offer at any given moment to maximize expected revenue. The resulting dynamic program for the firm is intractable and approximated by a deterministic linear program called the CDLP which has an exponential number of columns. However, under the choice-set paradigm...

Airlines must dynamically choose how to allocate their flight capacity to incoming travel demand. Because some passengers take connecting flights, the decisions for all network flights must be made simultaneously. To simplify the decision making process, most practitioners assume demand is deterministic and equal to average demand. We propose a multistage stochastic programming approach that mo...