The widespread impacts of artificial intelligence (AI) and machine learning (ML) in many
segments of society have not yet been felt strongly in the marketing field. Despite such …

Over the past two decades, there has been explosive growth in the application of robust
optimization in operations management (robust OM), fueled by both significant advances in …

We consider pricing and product-bundling strategies for two competing platforms with two
groups of agents, customers and sellers (independent content developers). In this paper …

We investigate the use of a canonical version of a discrete choice model due to Daganzo
(1979)[Daganzo C (1979) Multinomial Probit: The Theory and Its Application to Demand …

Revenue management (RM) is the application of analytical methodologies and tools that
predict consumer behavior and optimize product availability and prices to maximize a firm's …

Abstract Multimarginal Optimal Transport (MOT) has attracted significant interest due to
applications in machine learning, statistics, and the sciences. However, in most applications …

We review the operations research/management science literature on data‐driven methods
in retail operations. This line of work has grown rapidly in recent years, thanks to the …

Choice models are widely applied in psychology, economics, transportation, marketing, and
operations studies. We review the existing developments on the modeling of consumers' …

Customer behavior is often assumed to follow weak rationality, which implies that adding a
product to an assortment will not increase the choice probability of another product in that …

Most often, important decisions involve several unknown attributes. This produces a double
challenge in the sense that both assessing the individual multiattribute preferences and …