Ivey – Western Campus 2322
- Machine Learning
- Public Blockchain Technology
- Probability& Statistics
- Data Analytics
- Mathematical Modeling
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Aysajan Eziz (a.k.a. Aishajiang Aizezikali) is an Assistant Professor of Management Science at the Ivey Business School at Western University. He joined Ivey in 2018 from the Washington State University in US where he completed his Ph.D. in Operations & Management Science and M.S. in Statistics. Aysajan’s research focuses on a variety of interdisciplinary topics, including data-driven decision making in the context of pricing and revenue optimization, agricultural subsidy program analysis in developing countries, and public blockchain economic incentive mechanism analysis. Drawing on tools from optimization, machine learning, mathematical modeling, and statistics, his work aims to develop new analytical methods that have impact in both public and private sectors. Through his focus on mechanism analysis, Aysajan’s research addresses broader topics related to resource pricing, public policy evaluation, and public blockchain technology.
- Decision Making with Analytics (HBA1)
- Pricing and Revenue Analytics (MSc)
- Financial Analytics (MSc)
- PhD, Operations & Management Science, Washington State University
- MS, Statistics, Washington State University
- BA, Business Administration, Zhejiang University
Recent Refereed Articles
Baker, T.; Aizezikali, A.; Harrington, R. J., 2020, "Hotel Revenue Management for the Transient Segment: Taxonomy-Based Research", International Journal of Contemporary Hospitality Management, January 32(1): 108 - 125. Abstract: Abstract Purpose – This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate (1)-(2) into research stream recommendations for the open literature with an empirical focus. Design/methodology/approach – The authors use Nickerson’s taxonomy development method from the field of information systems to build the taxonomy. Findings – New forecasting areas include developing a metric for the degree of strategic fit of a hotel’s pricing strategy and using it in conjunction with quantifications of online reviews for predictions. New price optimization avenues include determining whether a lack of congruence between customer perceptions of fairness and trust and pricing history has a detrimental effect on overall hotel performance and determining which combinations of flexible products, decision-maker risk aversion, nonparametric forecasting and reference effect optimization features work best in which situations. Originality/value – This is the first study to combine vendor activities outside the technical realms of forecasting and price optimization with an emphasis on the choice modeling technical framework. This study points to several promising studies using qualitative methods, action research and design science.
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Pimental, V.; Aizezikali, A.; Baker, T., 2019, "Hotel revenue management: Benefits of simultaneous overbooking and allocation problem formulation in price optimization", Computers and Industrial Engineering, November 137(106073) Abstract: We develop a hotel revenue management optimization method in an environment where market segment prices are optimized via demand curves ahead of a planning horizon. This new method simultaneously optimizes overbooking levels and allocation (of capacity to market segments) levels, as opposed to the traditional sequential approach. We test our method against the reference in a simulation of a hotel reservation system that has all the functionality of a real-world revenue management system: the estimation of true demand from censored demand; different market segments with different demand patterns; price elasticities; varying propensities to stay certain lengths of time; short- and long-term forecasting with periodic reoptimization of all forecaster parameters; explicit optimization of market segment prices based on estimated demand curves; and optimization routines for overbooking and allocation. A walkthrough of this simulation was performed by the revenue management staff at a major hotel. This simulation has been scaled down to permit extensive experimentation. Our new method outperforms the reference method by an average of 20.2% with respect to nightly net revenue. The improvement is much larger in situations where demand is more saturated. Our new method takes less than two minutes of computing time from a cold start on a realistically sized problem, which is sufficiently fast for hotel managers who want the capability of rerunning the algorithm many times during the course of a day.
Link(s) to publication:
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: 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.
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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