J 2022

Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach

PETŘÍČEK, Martin, Štěpán CHALUPA, Jan MÁČE and Ivo STRAKA

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

Článek v odborném periodiku

Confidentiality degree

není předmětem státního či obchodního tajemství

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
Změněno: 16/3/2022 08:15, Ing. Štěpán Chalupa, Ph.D.

Abstract

ORIG EN

V originále

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.

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.
Displayed: 10/11/2024 01:54