مقاله انگلیسی رایگان در مورد روش های تجزیه برای تخصیص اتاق پویا در مدیریت درآمد هتل – الزویر 2018

Historically, the airline industry played the steering role in revenue management (RM). Today, however, there is a wide range of applications in different industries with volatile demand, requesting fixed and perishable capacity (Kimes, 1989). Although the hotel industry is one of the typical application areas of revenue management, the research in this particular area lags behind the work produced for other service industries. In their recent work, Ivanov and Zhechev (2012) and Ivanov (2014) present a review of the methods proposed in the hotel RM literature and point out the gaps. In general, well-known airline RM techniques, such as booking control and pricing, can be applied to hotel RM problems. However, it is important to consider several constraints that are unique to hotel reservation systems. First, multi-day stays in hotels are quite common. While a flight itinerary includes, on average fewer than three legs, the number of nights a typical customer spends in a hotel can be a week or even more (Zhang and Weatherford, 2017). Second, the demand process is different.  Hotel customers may decide to stay longer and extend their reservation while they are staying in the hotel (Kimes, 1989). Third, airline customers generally make advance bookings but a number of hotel customers consist of walk-ins. Moreover, the early reservations in the booking interval are even allowed to cancel their bookings at no extra cost. A reservation for a room spans several days, and hence, the number of available rooms in the hotel for any day is determined only by the reservations including that particular day. Therefore, the room allocation problem becomes the control of total room capacity when customer demand is characterized by the length of stay and the type of rooms. Although this setting leads to a special linear network structure, the dynamics of the problem are still quite challenging for analysis and optimization. The state space of the problem is the Cartesian product of daily capacities in the network. This is a wellknown difficulty in network RM problems. As a remedy, approximation methods based on problem decomposition are frequently used. The main idea behind these methods is to partition the network problem into independent subproblems. This idea has been widely adopted for network airline RM problems (Talluri and van Ryzin, 2004). However, it is important to note that these decomposition methods risk undermining the network effects of the shared daily capacities among different products (reservation types). To improve the performances of the decomposition methods, recent studies focus on protecting the network information by defining time and capacity dependent bid-prices and consider their effects on the network (Kunnumkal and Topaloglu, 2010; Zhang, 2011; Aslani et al., 2013). Bid price control is a widely adopted strategy in the airline revenue management literature. It is used to assess the value of allocating one capacity to a request. Basically, the bid price control sets a threshold price for each resource (e.g. flight leg, hotel day capacity). Then, a reservation request is accepted, only if its revenue exceeds the sum of the bid prices associated with the allocated resources. Vinod (2005) discusses that the bid price controls can be used as a price mechanism to determine the value of the product.