Dr. Zhang's current primary research interest is revenue management and pricing. In the last few years, he worked on approximate dynamic programming methods for large-scale dynamic optimization problems and on consumer behavior models with applications to revenue management and pricing. His research has been published in journals including Operations Research, Manufacturing and Service Operations Management, Production and Operations Management, Transportation Science, INFORMS Journal on Computing, European Journal of Operational Research, and Journal of Pricing and Revenue Management. Dr. Zhang served as a reviewer for several academic journals, and regularly presents at international conferences. Dr. Zhang received research grants from NSERC (Natural Sciences and Engineering Research Council of Canada), SSHRC (Social Sciences and Humanities Research Council of Canada), and FQRSC (Fonds de recherché Societe et Culture Quebec).
revenue management and pricing, supply chain management, healthcare operations, dynamic programming and optimization, stochastic modeling, game theoretical models, consumer behavior models
MSBX 5415 - Advanced Data Analytics
Spring 2019 / Spring 2020
Explores the capabilities and challenges of data-driven business decision making and prepares students to lead in analytics-driven organizations. Introduces a set of common predictive and prescriptive analytics tools. Students apply the analytics tools to important decisions based on practical data sets from various companies. Analytics software packages are used extensively in the course.
OPIM 7400 - Stochastic Dynamic Programming with Applications
Fall 2018 / Fall 2019
Covers the basic models and solution techniques for stochastic dynamic programs with finite or infinite number of stages. Application domains include, among other, revenue management and pricing, manufacturing, supply chains, service systems, and economics. Approximate solution techniques for problems involving large state/decision spaces and/or complex dynamics over time will also be discussed. Recommended requisite: an introductory course in optimization and probability.