PETŘÍČEK, Martin, Štěpán CHALUPA, Jan MÁČE and Ivo STRAKA. Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach. Quality - Access to Success. Bucharest: SRAC - Societatea Romana Pentru Asigurarea Calitatii, 2022, vol. 23, No 186, p. 151-155. ISSN 1582-2559. Available from: https://dx.doi.org/10.47750/QAS/23.186.19.
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Basic information
Original name Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach
Name (in English) Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach
Authors PETŘÍČEK, Martin, Štěpán CHALUPA, Jan MÁČE and Ivo STRAKA.
Edition Quality - Access to Success, Bucharest, SRAC - Societatea Romana Pentru Asigurarea Calitatii, 2022, 1582-2559.
Other information
Type of outcome Article in a journal
Confidentiality degree is not subject to a state or trade secret
Organization unit University College Prague – University of International Relations and Institute of Hospitality Management and Economics, Ltd.
Doi http://dx.doi.org/10.47750/QAS/23.186.19
Tags International impact, Reviewed
Changed by Changed by: Ing. Štěpán Chalupa, Ph.D., učo 10522. Changed: 16/3/2022 08:15.
Abstract
This paper discusses the problem of hotel room sales, emphasizing the allocation of a specific amount of rooms for predefined market segments using the extension of the Expected Marginal Seat Revenue model, which can be perceived as one of the crucial activities in modern revenue management. The proposed extension of this model can improve data-based decision-making quality and the service provider's overall performance. This model uses stochastic non-linear programming to solve the problem of room allocation for the market segment and deals with the booking limits to help decide whether to accept or decline the booking request based on the behaviour of selected market segments or the expected current state of available capacities and other elements. To extend the model to fit better the managerial decision-making, the factors of no-shows and cancellations are implemented. The proposed model should be used to strengthen the quality of revenue management-focused decisions mainly focused on dynamic pricing, and the room allocation is omitted. The model respects the need for a more comprehensive perception of the problem and contains data-based factors like quantifying customer (or customer segment) behaviour and no-shows and cancellations that affect the selling strategy.
Abstract (in English)
This paper discusses the problem of hotel room sales, emphasizing the allocation of a specific amount of rooms for predefined market segments using the extension of the Expected Marginal Seat Revenue model, which can be perceived as one of the crucial activities in modern revenue management. The proposed extension of this model can improve data-based decision-making quality and the service provider's overall performance. This model uses stochastic non-linear programming to solve the problem of room allocation for the market segment and deals with the booking limits to help decide whether to accept or decline the booking request based on the behaviour of selected market segments or the expected current state of available capacities and other elements. To extend the model to fit better the managerial decision-making, the factors of no-shows and cancellations are implemented. The proposed model should be used to strengthen the quality of revenue management-focused decisions mainly focused on dynamic pricing, and the room allocation is omitted. The model respects the need for a more comprehensive perception of the problem and contains data-based factors like quantifying customer (or customer segment) behaviour and no-shows and cancellations that affect the selling strategy.
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