KEYWORDS: hotels, forecasting, revenue management, network flow Over the past decade, revenue management techniques have been extensively developed in the airline and hotel industries. Much of the research has been on the optimisation front, which focuses on finding the optimal seat allocation policy to maximise revenue. There has been, however, less published work on forecasting issues. In this paper, we present a framework for forecasting and optimisation which we apply to the hotel industry. Based on real-life hotel booking data, we utilise various forecasting techniques and compare their performance. Because classical stochastic optimisation models are generally too hard to solve, we present a network flow formulation and report on computational results.

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In this article, we explore the current‐state of literature on how dynamic pricing models incorporate consumer reference‐price effects in developing more informed dynamic pricing strategies for products that have repeated consumer interactions. We first examine the literature on how consumer demand is impacted by reference‐price effects and how consumers form reference prices through their purchase experience. We then explore, in detail, the dynamic pricing models with consumer reference‐price effects in major studies, in order to highlight their key findings and insights. We conclude our review by pointing out research gaps and future research directions on dynamic pricing area in the presence of consumer reference‐price effects.

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