Editorial: 2003 — My predictions for revenue management

A Genetic Algorithm for Choice-Based Network Revenue Management

In recent years, enriching traditional revenue management models by considering the customer choice behavior has been a main challenge for researchers. The terminology for the airline application is used as representative of the problem. A popular and an efficient model considering these behaviors is choice-based deterministic linear programming (CDLP). This model assumes that each customer bel...

Revenue Optimization with Approximate Bid Predictions

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. W...

Revenue Management for Transportation Problems

In this paper, we consider the problem of managing a fleet of trucks with different capacity to serve the requests of different customers that arise randomly over time. Customers make a request by demanding the transportation of a given quantity of goods from an origin to a destination, at a given time. It is well known that these problems arise in the dynamic resource allocation context, where...

Editorial: Why was my manuscript rejected?

Blind Network Revenue Management

We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underl...