Ivey – Western Campus 2322
- Revenue Management
- Stochastic Approximation
- Business Analytics
- Business Modeling with Spreadsheets
- Visual Basic for Applications (VBA)
- To search for publications by a specific faculty member, select the database and then select the name from the Author drop down menu.
Aysajan Eziz (a.k.a. Aishajiang Aizezikali) is an Assistant Professor of Management Science at the Ivey Business School. Aysajan’s primary research focuses on revenue management and pricing optimization, specifically on the areas of hotel revenue management.
- Decision Making with Analytics (HBA)
- Financial Analytics (MSc)
- PhD, Operations & Management Science, Washington State University
- MS, Statistics, Washington State University
- BA, Business Administration, Zhejiang University
Recent Refereed Articles
Pimentel, V.; Aizezikali, A.; Baker, T.,
2018, "An evaluation of the bid price and nested network revenue management allocation methods", Computers and Industrial Engineering, January 115: 100 - 108.
Abstract: © 2017 Elsevier Ltd We compare the revenue generating capabilities of the bid price allocation method and the nested network method in hotel revenue management. Revenue maximization is achieved by an optimal allocation of assets across market segments, subject to constraints such as overbooking limit and the cross-elasticity of competitors’ pricing. Using a simulation model of a large hotel's reservation system, validated by Marriott hotels, we find that the nested network method outperforms the bid price method, and, on average, leads to an improvement of 6% in revenue in the worst-case scenarios across operating environments. This improvement is 3.6% when restricted to cases in which overbooking and allocation are performed simultaneously. In no operating environment is the improvement less than 2%. Since the bid price method is, by far, the most commonly used allocation method in practice, these results indicate that hotels should consider switching to the nested network method. This change is feasible because (1) most hotels already have in place the core optimization system required to execute the nested network method, and (2) the nested network method converges to optimality in less than two minutes for most realistically sized problems, as we demonstrate.
Link(s) to publication:
Honours & Awards
- CISER Alumni Award for Best MS Statistics Project, Department of Mathematics and Statistics, Washington State University, 2017
- Outstanding Graduate Student Research Award, Carson College of Business, Washington State University, 2018
- Instructor, Washington State University, 2015-2018