Sabre Corporation

About: Sabre Corporation is a based out in . It is known for research contribution in the topics: Revenue management & Yield management. The organization has 31 authors who have published 43 publications receiving 1939 citations.

Estimating Primary Demand for Substitutable Products from Sales Transaction Data

Abstract: We propose a method for estimating substitute and lost demand when only sales and product availability data are observable, not all products are displayed in all periods (e.g., due to stock-outs or availability controls), and the seller knows its aggregate market share. The model combines a multinomial logit (MNL) choice model with a non-homogeneous Poisson model of arrivals over multiple periods. Our key idea is to view the problem in terms of primary (or first-choice) demand; that is, the demand that would have been observed if all products had been available in all periods. We then apply the expectation-maximization (EM) method to this model, and treat the observed demand as an incomplete observation of primary demand. This leads to an efficient, iterative procedure for estimating the parameters of the model, which provably converges to a stationary point of the incomplete data log-likelihood function. Every iteration of the algorithm consists of simple, closed-form calculations. We illustrate the effectiveness of the procedure on simulated data and two industry data sets.

OM Practice---Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization

Abstract: Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline. Our study had two main objectives: (1) to assess the extent to which choice models can be estimated well using readily available airline data, and (2) to gauge the potential impact that choice-based RM could have on a sample of test markets. We developed a maximum likelihood estimation algorithm that uses a variation of the expectation-maximization method to account for unobservable data. The procedure was applied to data for a test market from New York City to a destination in Florida. The outputs are promising in terms of the quality of the computed estimates, although a large number of departure instances may be necessary to achieve highly accurate results. These choice model estimates were then used in a simulation study to assess the revenue performance of the EMSR-b (expected marginal seat revenue, version b) capacity control policies and the current controls used by the airline relative to controls optimized to account for choice behavior. Our simulation results show 1%--5% average revenue improvements using choice-based RM. Although such simulated results must be taken with caution, overall our study suggests that choice-based revenue management is both feasible to execute and economically significant in real-world airline environments.

Estimating Primary Demand for Substitutable Products from Sales Transaction Data

Abstract: We propose a method for estimating substitute and lost demand when only sales and product availability data are observable, not all products are displayed in all periods (e.g., due to stockouts or availability controls), and the seller knows its aggregate market share. The model combines a multinomial logit (MNL) choice model with a nonhomogeneous Poisson model of arrivals over multiple periods. Our key idea is to view the problem in terms of primary (or first-choice) demand; that is, the demand that would have been observed if all products had been available in all periods. We then apply the expectation-maximization (EM) method to this model, and we treat the observed demand as an incomplete observation of primary demand. This leads to an efficient, iterative procedure for estimating the parameters of the model. All limit points of the procedure are provably stationary points of the incomplete data log-likelihood function. Every iteration of the algorithm consists of simple, closed-form calculations. We illustrate the effectiveness of the procedure on simulated data and two industry data sets.

Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs

Abstract: We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorithm by using dual prices that are calculated during execution of the standard PHA. We report computational results on stochastic unit commitment and stochastic server location problem instances, and explore the relationship between key PHA parameters and the quality of the resulting lower bounds.

A General Attraction Model and Sales-Based Linear Program for Network Revenue Management Under Customer Choice

Abstract: This paper addresses two concerns with the state of the art in network revenue management with dependent demands. The first concern is that the basic attraction model BAM, of which the multinomial logit MNL model is a special case, tends to overestimate demand recapture in practice. The second concern is that the choice-based deterministic linear program, currently in use to derive heuristics for the stochastic network revenue management problem, has an exponential number of variables. We introduce a generalized attraction model GAM that allows for partial demand dependencies ranging from the BAM to the independent demand model IDM. We also provide an axiomatic justification for the GAM and a method to estimate its parameters. As a choice model, the GAM is of practical interest because of its flexibility to adjust product-specific recapture. Our second contribution is a new formulation called the sales-based linear program SBLP that works for the GAM. This formulation avoids the exponential number of variables in the earlier choice-based network RM revenue management approaches and is essentially the same size as the well-known LP formulation for the IDM. The SBLP should be of interest to revenue managers because it makes choice-based network RM problems tractable to solve. In addition, the SBLP formulation yields new insights into the assortment problem that arises when capacities are infinite. Together these contributions move forward the state of the art for network revenue management under customer choice and competition.