| Presenter |
Title |
Institution |
Abstract |
| Jonas Rauch |
A Practical Perspective on “Disentangling Capacity Control from Price Optimization"
|
PROS |
In 2017, we (J. Rauch, K. Isler, S. Pölt) published a paper titled “Disentangling Capacity Control from Price Optimization,” demonstrating that Revenue Management (RM) can be mathematically divided into two distinct problems: pricing and capacity control. We showed that it is theoretically possible to solve the capacity control problem without extensive knowledge of the pricing mechanism or detailed price data. However, our technical approach in the paper and conference presentations did not effectively argue why one would want to adopt this method. In this presentation, we revisit this question from a practical and intuitive standpoint, highlighting the circumstances under which a disentangled approach is advantageous. |
Kalyan Talluri Hanzhao Wang Xiaocheng Li |
Gen AI architectures for RM estimation |
Imperial College London Business School |
We outline our experience with using GenAI models to capture a number of sequential decision-making tasks such as dynamic pricing, inventory management, resource allocation, and queueing control. Under this framework, all these tasks can be viewed as a sequential prediction task where the goal is to predict the optimal future action given all the history information. When adequately trained this approach can offer distinct advantages over any existing models. We present our experiments and experience specifically on dynamic pricing and RM estimation. |
| Clay Youngblood |
Outside-of-RMS Machine Learning: Boost RM Decision Making by Thinking Outside the Box |
Southwest Airlines |
With the rise of open-source machine learning, airlines have greater opportunities to build in-house models that can complement or extend their revenue management systems. Such models can accurately predict important KPIs that facilitate smarter decision making. However, the booking data that practitioners will use to build said models is ripe with bias. In this presentation, we explore the consequences of constructing a ML model that learns a spurious correlation between bid prices and flown load factors – two variables that are correlated, but ultimately influenced by broader “demand” factors. We illustrate problems with the constructed model and propose, as a solution, a method of omitting inventory metrics all together to avoid the creation of the problematic relationships. Therefore, creating models only guided by a phenomenon we call implied revenue management strategy. We then dive into empirical examples of this methodology working at Southwest. |
Müge Tekin Kalyan Talluri |
Estimation using marginal competitor sales information |
Erasmus University Rotterdam School of Management |
An abiding concern for firms is how customers value their product compared to the competitor’s. Estimating this is challenging as, even though prices are public, competitors’ sales are typically unobservable. However, in hotel industry, marginal aggregated competitor sales data can be obtained through STR reports. Hotels participate by reporting their sales and in turn receive aggregated competitor data across groups and LOS. Such data is rarely used in RM estimation due to lack of robust methodologies. This paper tackles this problem under a market-share model for hotels, addressing key challenges: (i) competitor data is aggregated across LOS with distinct demands, (ii) no-purchasers are unobserved, and (iii) competitor group sales and capacity remain private. Using Monte Carlo simulations, our method recovers true parameters from synthetic data. We then apply it to real-bookings. Our method surpass alternate estimation methods from NT and RM literature. |
| Saar Teboul |
Generative AI & Large Market Models: Transforming Airline Revenue Management through Multi-Objective Optimization |
Fetcherr |
This study leverages generative AI and large market models to revolutionize airline revenue management by applying multi-objective optimization to the complex dynamics of pricing. Focusing on the balance between revenue, load factor, and competitive positioning, we introduce a framework to showcase the trade-offs between these key performance indicators, we demonstrate how airlines can utilize advanced AI-driven models to adjust fares strategically, optimizing seat occupancy, profitability, and market competitiveness in an ever-evolving landscape. |
|
Charles Pierre
Mathias Lecuyer
|
Training and Evaluating Causal Forecasting Models for Revenue Management Time-Series |
WIREMIND |
We leverage deep learning time-series models to make forecasts that inform downstream decisions. Since these decisions can differ from those in the training set, there is an implicit requirement that time-series models will generalize outside of their training distribution. In reality this requirement is really hard to attain as the historical data only contain a very small subset of all possible prices. Thus naive model will have difficulties infering price elasticity and generalized outside the historical distribution of price. We extend orthogonal statistical learning framework to train causal time-series models that generalize better when forecasting the effect of actions outside of their training distribution. We leverage Regression Discontinuity Designs popular in economics to construct a test set of causal treatment effects. |
| Gopal Ranganathan |
A cutting-edge AI design using a Quantitative Large Learning Model Q-LLM) for Enterprise Revenue Management |
QuadOptima |
A cutting-edge AI design using a Quantitative Large Learning Model Q-LLM) for Enterprise Level Revenue Management is presented, to address emerging challenges like NDC protocols. These challenges require classless retailing, bundling and continuous dynamic pricing. The design advances RM by blending the analytical approach of operations research with machine learning based transformer approach to handle large multidimensional data. The transformer features a DNA Analyzer , forecast and optimization engines. It also features a micro-segment data model to facilitate handling the transformer architecture. The design drives better tradeoffs in Global vs Local optima and increases profits and long-term value. An example application of dynamic pricing using this new modern RM design is presented. |
Vladimir Antsibor Marc Nientker |
Estimating willingness-to-pay under unobserved confounders |
ADC (acmetric) |
The airline industry is shifting from class-based to dynamic pricing, requiring accurate willingness-to-pay (WTP) estimation. This demands causal inference techniques to address endogeneity from unobserved confounders. Traditional methods, such as instrumental variables, rely on strong, non-verifiable assumptions, while newer debiased machine learning approaches attempt to account for all relevant influences. However, real-world complexities make it difficult to anticipate and incorporate all confounders. We introduce a modern econometric method for WTP estimation that models confounders' broader impact on itineraries, enabling robust estimation without prior knowledge of their exact nature. Our validated approach helps airlines refine pricing strategies while addressing real-world complexities. We will discuss the theory, empirical validation, and revenue management implications. |
| Burak Ozdaryal |
Rethinking Price Elasticity: The Competitive Dimension |
Sabre |
Traditional approaches to price elasticity of demand in airline revenue management often operate under the assumption of independence of demand across different offerings. This presentation challenges this assumption by examining the impact of competitive product offerings. We introduce a slight modification to the traditional q-forecasting framework that incorporates these competitive effects to illustrate the necessity to re-evaluate traditional pricing strategies and marginal revenue transformation. Our analysis suggests that neglecting the competitive landscape can lead to suboptimal pricing decisions. This talk will explore the limitations of conventional models to stimulate thought on developing better revenue management techniques for today's dynamic marketplace. |
Richard Ratliff Helder Inacio Xiaoyun Niu Keji Wei |
A Stochastic SBLP Optimizer for Network RM with Dependent Demands |
Sabre
|
We will present a new model innovation for solving stochastic, class-based, airline network revenue management problems under dependent demands in a computationally efficient manner that optimizes item allocations. Our approach uses simulation-based, linear-programming optimization (with dependent demand, spill, and recapture effects) based on sample average approximation (SAA). Our overview in this presentation includes the model formulation along with applied examples and computational results. |
| David Foster |
A survey of cargo revenue management problems |
Delta Air Lines
|
Cargo Revenue Management differs in substantial ways from Passenger RM, however it is poorly understood and studied. This paper illustrates some of the differences between the two areas and proposes several avenues of future investigation. |