6533b832fe1ef96bd129a4c9
RESEARCH PRODUCT
An integrated fuzzy-stochastic model for revenue management: The hospitality industry case
Valerio LacagninaDavide Provenzanosubject
Service (business)021103 operations researchRevenue managementbusiness.industryStochastic modellingGeography Planning and DevelopmentFuzzy optimization stochastic demand revenue management booking system hospitality industry.0211 other engineering and technologies02 engineering and technologySettore SECS-P/06 - Economia ApplicataHospitality industryFuzzy logicTask (project management)Product (business)Settore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Tourism Leisure and Hospitality Management0202 electrical engineering electronic engineering information engineeringEconomicsbooking system fuzzy optimization hospitality industry revenue management stochastic demand020201 artificial intelligence & image processingOperations managementbusinessIndustrial organizationdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-07-21 |