Revenue Management (RM) refers to the theory and practice of IT-supported management of demand by means such as prices or product availability based on demand models so as to maximize profits or revenue. As a discipline, it originated in the airline industry in the 1970s following the deregulation of the US airline market. The term RM was coined because, in the airline sector, low variable costs and high fixed costs lead to an environment where maximization of revenue is (almost) equivalent to maximizing profits. However, RM techniques are not limited to revenue optimization, and indeed are also used for maximization of other objectives such as profit. RM can be delineated from more general pricing practice by its use of often highly sophisticated computer systems that automatically process sales and other data to produce demand forecasts, which in turn are used to optimize demand management decisions. Since its conception, it has seen widespread adoption in many areas, including transportation (trains, car rental, ferries, cargo shipping), hospitality (hotels, casinos), broadcasting and advertising, and others. A lot of research has been dedicated to this field over the past 40 years; for relevant literature reviews see McGill and Van Ryzin (1999) and Chiang et al. (2007), for relevant textbooks see Talluri and Van Ryzin (2004), Phillips (2005), or Bodea and Ferguson (2014). Until around 2004, most of it was based on the assumption that demand for any product is independent of the availability of other products. In other words, it was assumed that customers would never substitute one product for another, but instead would consider the purchase of a specific product only and, if this was not available, not to purchase at all. This so-called independent demand assumption was and still is reasonable in quasi-monopolistic situations where different product offerings are strongly fenced off. For example, in the airline industry, different types of fares used to be associated with specific restrictions (cancelation policy, advance purchase requirement, . . . ) that were intended to appeal to a specific customer segment only. However, with the rise of low-cost carriers and the advent of online travel agencies like Expedia or Orbitz by the early 21st century, the independent demand assumption turned out to be problematic due to the increased competition and visibility of different fares. Consequently, the focus of this review is on the design and estimation of discrete choice models for RM, and on the problem of how to control demand via product availability over a finite period of time while accounting for customer’s choice behavior. The most recent comprehensive review in this field is by Shen and Su (2007), who provide an overview of research on customer behavior in RM and auctions. More specifically, they partition work in the area in those papers dedicated to inter-temporal substitution and strategic customer purchase behavior, and those focusing on multi-product substitution. We exclude the former from our review and only take multi-product substitution into account.