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INTRODUCTION
Revenue management is the maximisation of revenue by 'selling the right seats to the right customer at the right time' (American Airlines 1987, as cited in Weatherford and Bodily, 1992, p 832). This definition was subsequently modified to include 'and at the right price' (ie to add the option of multiple prices) (Kimes, 1989; Pak and Piersma, 2002; Kimes and Thompson, 2004; Yeoman et al. , 1999; Upchurch et al. , 2002).
Over the past 30 years, revenue management research has evolved in scope. Beginning in the 1970s, Rothstein (1971, 1974) and Littlewood (1972) investigated practices of revenue management in airlines and hotels. With the deregulation of the airline industry in 1978, more research followed, leading to the seminal papers of Belobaba (1987a, 1987b, 1989) that propelled revenue management into mainstream operations research (OR). At that point in time however, the understanding of revenue management was largely on a computational and operational level, with literature dominated by operations researchers (Desiraju and Shugan, 1999). Hence, the scope of revenue management was limited to capacity planning and allocation, for a given set of prices. Gradually, it became evident that revenue management research should factor in the pricing policies of firms, and also make demand or consumer behaviour endogenous to revenue management (Fleischmann et al. , 2004; Ng, 2007). It also became clear that the practice of revenue management was applicable to other service firms besides airlines and hotels, which led to research papers on revenue management in industries such as car rental and internet service providers (eg Carroll and Grimes, 1995; Nair and Bapna, 2001).
With the advent of the internet and other advances in technology, revenue management also became increasingly complex (Elmaghraby and Keskinocak, 2003). Great leaps in computation power allowed for more complex optimising algorithms to emerge, while the internet made it possible to constantly collect the data necessary for the generation of better forecasts to aid firms in both capacity allocation and pricing. This gave rise to the possibility of instantaneous decision making, enabling revenue management systems to be more efficient and responsive. Also, in the past, demand data were far more difficult to obtain and less systematic to process, and as a result supply-driven revenue management was a natural research orientation. With...