Prominent hotel chains quote a booking price for a particular type of rooms on each day and dynamically update these prices over time. We present a novel Markov decision process (MDP) formulation that determines the optimal booking price for a single type of rooms under this strategy, while considering the availability of rooms throughout the multiple-day stays requested by customers. We analyze special cases of our MDP to highlight the importance of modeling multiple-day stays and provide guidelines to potentially simplify the implementation of pricing policies around peak-demand events such as public holidays and conferences. Since computing an optimal policy to our MDP is intractable in general, we develop heuristics based on a fluid approximation and approximate linear programming (ALP). We numerically benchmark our heuristics against a single-day decomposition approach (SDD) and an adaptation of a fixed-price heuristic. The ALP-based heuristic (i) outperforms the other methods; (ii) generates up to 7% and 6% more revenue than the SDD and the fixed-price heuristic respectively; and (iii) incurs a revenue loss of only less than 1% when using our pricing structure around peak-demand events, which supports the use of this simple pricing profile. Our findings are potentially relevant beyond the hotel domain for applications involving the dynamic pricing of capacitated resources.
Hotel Revenue Management, Resource Pricing, Markov Decision Processes, Approximate Linear Programming
Hospitality Administration and Management | Operations and Supply Chain Management
NADARAJAH, Selvaprabu; LIM, Yun Fong; and DING, Qing.
Dynamic pricing for hotel rooms when customers request multiple-day stays. (2015). 1-42. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/5147
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