ESS: Dynamic and Stochastic Network Flow Models for Robust Revenue Optimization in Hotel Service Sector

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The research from this project is expected to produce significant methodological contributions to enhance revenue management practices through reservations control, in hotels and other service industries. In particular, to increase revenue potential, this project investigates the possibility of bridging the revenue gap between ideal revenue solution (if all requests were known ahead of time) and the current practice of maximizing expected revenue. To enhance the realism of demand assumptions, a statistical framework for modeling customer reservation decisions (consumption, cancellation, and duration) will be proposed. The project will also develop robust initial solutions by determining ideal revenue solutions for several demand request scenarios. Further, this optimal apriori solution will be updated over time as current reservation requests are revealed, thus accounting for discrepancies between forecasted and actual demands. The research findings and models will ultimately lead to the development of significantly improved and more robust revenue optimization practices in a variety of service industry networks, by synthesizing interdisciplinary principles from networks, reliability, algorithms, simulation, and forecasting to develop new models and tools for reservations control in the hotel industry. Benefits to various segments of the society include: (1) decision support tools for reservations managers; (2) improved revenues and revenue reliability for hotels and airlines; (3) health care service planning; and (3) reservations control for special events (e.g. concerts). In service sector industries such as airlines, hotels and rental car agencies, simple heuristic and empirical tools have been applied for reservations control. These current models are too restrictive in their assumptions, and do not account for the complexity of the revenue management problem. This project aims to develop methods for realistic modeling of the reservations problem, thus leading to increased revenue potential and robustness, through a network flow-based approach. To achieve these objectives, this project aims to: (1) develop a disaggregate framework to model the demand for reservations over time; (2) propose dynamic network models to obtain robust optimal solutions to the real-time reservations control problem; and (3) develop network-based models to maximize the robustness of revenues under uncertain arc costs (due to cancellation, no shows etc.). A graduate level course on 'Reliability and Optimization of Dynamic and Stochastic Infrastructure and Service Networks' will be developed to prepare leaders in research and practice in various service and infrastructure industries operating complex physical and/or virtual networks. The project will also aggressively recruit female and minority students to enhance the diversity of engineers in the service sector.