A central problem of interest to operations managers is how to use historical sales data to accurately predict revenues when offering a particular assortment of products. Such predictions are subsequently used in making important business decisions such as assortment planning, new product development, and demand estimation. Choice models are widely used in modeling the underlying customer behavior. Traditional choice models are either too simple to accurately reflect the nature of how people make decisions, or so complex that it is either computationally intractable to fit the model to historical data or to subsequently use it to make business decisions. This EArly-concept Grant for Exploratory Research (EAGER) project studies innovative and novel choice models that are designed to be computationally efficient for the decision problems of interest in revenue management and at the same time have strong predictive power. The results of the research will enable improved business decisions to be made while simultaneously reducing operational costs. It will provide key technologies in important business applications of assortment planning, new product development, brand value evaluation, demand estimation, optimal pricing, and revenue management. It will generate collaborations among the disciplines of operations management, economics, cognitive psychology, and machine learning. The research component is tightly integrated with the education plan, including a graduate course on probabilistic graphical models. Both undergraduate and graduate students will benefit from the research activities by engaging in research and applying the knowledge to solve real world problems.
The suboptimal tradeoff of traditional choice models is due to the fact that these models are designed without computational efficiency in mind. In this era of tremendous increase in the scale of data being generated, computational efficiency is of primary concern. This project will build on graph-based probabilistic models such as random walks on graphs and probabilistic graphical models, and will lead to (a) development of new graph-based models for choice modeling designed for computational efficiency; (b) development of new methodologies for learning these models from historical purchase data; (c) development of novel inference algorithms for predicting the customer preferences from these models; and (d) development of new methodologies for solving optimization problems in revenue management with these models. The research will lay foundations of a new graph-based modeling approach for revenue management. The significance and novelty of the work lie in the fact that the design objective of the choice modeling is critically different from the traditional criteria used by economists and cognitive psychologists (such as describing the functional form of the underlying rational decision processes), which does not consider the computational efficiency of solving decision problems in revenue management. In contrast to this, choice models for making decisions based on massive modern datasets should have computational efficiency embedded into the models by design. The theory and models developed in this project will bring together ideas and techniques from probability theory and graph theory to jointly reason about uncertainty and complexity (such as probabilistic graphical models and random walks on graphs) as well as insights and tools from recent advances in revenue management (such as choice modeling using Markov chains). The research has a potential to advance our fundamental understanding in how people make decisions when presented with many options.
