Models and Techniques for Hotel Revenue Management Using a Roling Horizon

AbstractThis paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.mathematical programming;Revenue Management;yield management

Models and techniques for hotel revenue management using a rolling horizon.

This paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.Revenue management;Mathematical programming;Yield management

Models and techniques for hotel revenue management using a rolling horizon.

This paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays

Decomposition methods for dynamic room allocation in hotel revenue management

Long-term stays are quite common in the hotel business. Consequently, it is crucial for the hotel managements to consider the allocation of available rooms to a stream of customers requesting to stay multiple days. This requirement leads to the solving of dynamic network revenue management problems that are computationally challenging. A remedy is to apply decomposition approaches so that an approximate solution can be obtained by solving many simpler problems. In this study, we investigate several room allocation policies in hotel revenue management. We work on various decomposition methods to find reservation policies for advance bookings and stay-over customers. We also devise solution algorithms to solve the resulting problems efficiently

An integrated fuzzy-stochastic model for revenue management: The hospitality industry case

Revenue management aims at improving the performance of an organization by selling the right product/service to the right customer at the right time. This task is very dependent on uncontrollable external factors. In the hospitality industry, rooms of the hotel represent perishable assets and fixed capacities at the same time. Therefore, in the case of a stochastic process for customers calling in reservations prior to a particular booking date, a common problem for hotels is to devise a policy for maximizing the total expected profit conditional on the set of bookings. We propose a fuzzy model for the hotel revenue management under an uncertain and vague environment. Fuzziness of objective and constraint functions have been incorporated into a stochastic booking model considering multiple-day stays to show the effect of uncertainty on the optimal demand. By changing the relaxation parameters of the objective function, we have found a set of optimal solutions with, in most of the cases, a value of the objective function equal to the optimal solution of the stochastic model, providing several alternative optimal room allocations

Technology revenue management system for customer groups in hotels

This paper discusses revenue management; a technique that focuses on decision making that will maximize profit from the sale of perishable inventory units. New technologies management plays an important role in the development of revenue management techniques. Each new advance in technology management leads to more sophisticated revenue business capabilities. Today decision support revenue management systems and technologies management are crucial factors for the success of businesses in service industries. This paper addresses the specific case of customer groups in hotels.The paper introduces a new decision support system that sets the revenue maximization criteria for a hotel. The system includes a set of forecasting demand methods for customers. It addresses a general case considering individual guests and customer groups. The system also incorporates deterministic and stochastic mathematical programming models that help to make the best decisions. The actual revenue depends upon which reservation system the hotel uses. A simulation engine makes a comparison between different heuristics of room inventory control: the results include performance indexes such as occupancy rate, efficiency rate, and yield; it compares results and chooses one of them. The system proves its suitability for actual cases by testing against actual data and thus becomes an innovative and efficient tool in the management of hotels’ reservation systems

Hotel sales and reservations planning

Includes bibliographical references (p. 26-27).Research partially supported by the Leaders for Manufacturing Program.Gabriel R. Bitran, Thin-Yin Leong

Airline Revenue Management with Shifting Capacity

Airline revenue management is the practice of controlling the booking requests such that the planes are filled with the most profitable passengers. In revenue management the capacities of the business and economy class sections of the plane are traditionally considered to be fixed and distinct capacities. In this paper, we give up this notion and instead consider the use of convertible seats. A row of these seats can be converted from business class seats to economy class seats and vice versa. This offers an airline company the possibility to adjust the capacity configuration of the plane to the demand pattern at hand. We show how to incorporate the shifting capacity opportunity into a dynamic, network-based revenue management model. We also extend the model to include cancellations and overbooking. With a small test case we show that incorporating the shifting capacity opportunity into the revenue management decision indeed provides a means to improve revenues.convertible seats;dynamic capacity management;revenue management;seat inventory control;shifting capacity