This paper is published in Volume-4, Issue-2, 2018
Area
Hospitality and Tourism
Author
Gopi Nath Vajpai
Org/Univ
Renaissance College of Hotel Management and Catering Technology, Ramnagar, Uttarakhand, India
Pub. Date
07 April, 2018
Paper ID
V4I2-1655
Publisher
Keywords
Overbooking, Revenue Management, Room Reservation, Poisson distribution.

Citationsacebook

IEEE
Gopi Nath Vajpai. Managing overbooking in hotels: A probabilistic model using Poisson distribution, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Gopi Nath Vajpai (2018). Managing overbooking in hotels: A probabilistic model using Poisson distribution. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.

MLA
Gopi Nath Vajpai. "Managing overbooking in hotels: A probabilistic model using Poisson distribution." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.

Abstract

Yield Management is commonly practiced in all hotels and other industries where advance booking is required. Various revenue and non-revenue tools of yield management have been practiced and applied. One important tool for yield management is using overbooking in hotels and airlines industries. For management at an operational level, a sound understanding of overbooking will allow maximizing revenue. The real challenge arises when faced with the question regarding how many overbooking is optimum. If we are overly cautious and overbook very less, it would mean a loss of revenue for the hotel. On the other hand, if we overbook to a large extent it could result in confirmed reservations being walked (turned away). This may lead to unsatisfied guests and a poor brand image for the hotel. Hence a balanced approach is required which will help us optimize the overbooking level. In most hotels, this is achieved by adjusting overbooking level based on the experience of Front Office manager. This paper utilizes Poisson distribution as a tool to model the probability of no-shows in a hotel using historical data and hence optimize the number of overbooking to be accepted in a given time period.  
Paper PDF