An integrated fuzzy-stochastic model for revenue management

Abstract

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.

Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty

“…Since the accurate prediction of demand has a significant role in reducing food waste (Pirani and Thompson, 2016), the present study consciously decided to focus on this issue. In the field of demand estimation with uncertainty in reservation systems, several studies have been conducted, mainly focusing on the fields of hotel management (Fiori and Foroni, 2020; Lacagnina and Provenzano, 2016) and transportation planning (Gao and Le, 2018; Luo and Shi, 2006); however, the present research takes a university reservation system as a case study and aims to accurately estimate the exact amount of meals needed based on the booking data and demand-side information. Although several studies have been conducted in the field of reservation systems (Tsai, 2014) and their relationship with food waste management (Ongkunaruk and Kessuvan, 2013), designing and implementing a demand estimation model under uncertainty in order to manage and reduce food waste in university self-services has received scant attention.…”