AhnHyunsoo

Hyun-soo Ahn

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Hyun-soo Ahn is an Associate Professor of Operations and Management Science at the Michigan Business School. He joined Michigan in 2003 from the department of Industrial Engineering and Operations Research at UC Berkeley. In his research, Hyun-soo develops and analyzes mathematical models related to supply chain management, dynamic pricing and revenue management, workforce agility, and resource allocation. He is also interested in modeling the customer’s behavior (such as subscription) and how it affects the firm’s profit. He has worked with more than 20 companies and his research has been funded by several organizations including National Science Foundation. His papers appear in leading journals in the field, including Operations Research, M&SOM, and Journal of Applied Probability.

At Ross, he teaches supply chain analytics and business statistics to MBAs, Exec. MBAs, MSCM, and BBAs. He has won a number of teaching awards voted by students, including 2012 Exec MBA teaching excellence award, 2012 Global MBA teaching excellence award, and 2006 BBA teaching excellence award.

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Siqian Shen

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The research of Shen’s group covers the following aspects that are closely related to data science and decision making.

  1. We develop modeling techniques and computational paradigms for complex service systems that incorporate a variety of information and decisions. We design risk optimization approaches that are capable of handling integrated system design and service operations with multiple resources, multiple stages of service, and multiple stakeholders with diverse decision preferences under data uncertainty.
  2. We develop new decomposition paradigms for stochastic integer programming models. We focus on two-stage stochastic integer programs, and advance decomposition paradigms based on special structures of specific risk-averse programs, and also based on special integer-programming structures. We have implemented the related approaches in a series of research projects that optimize stochastic problems of (i) project management, (ii) resource allocation, (iii) appointment scheduling, and (iv) network interdiction. Moreover, the new decomposition paradigms can be widely applied to large-scale complex system design and operations management, including optimizing critical interdependent infrastructures such as power grids, transportation systems, and cyber-clouds.
  3. We study network interdiction models and design algorithms for specially structured networks (e.g., trees, small-world networks) in defense-related problems. Dynamic programming and heuristic approaches are used for deriving solutions in polynomial time, and generating valid bounds to the corresponding stochastic optimization models.
  4. With increasing penetrations of fluctuating renewable energy sources, such as wind power plants and solar photovoltaics, and active participation of electric loads in power system operation, uncertainty will increase. Specifically, we develop data-driven and distribution-free optimization methods that are suited to dispatching power systems with both fluctuating renewable energy sources and flexible loads contributing to balancing reserves via load control. The flexibility of an aggregation of loads is difficult to compute and uncertain. Investigating, characterizing, and managing this uncertainty is the focus of this research. Moreover, we quantify the tradeoff between the uncertainty and profitability of load control, and the effect of uncertainty and methods for managing it on power system dispatch, which affects pollutant emissions.
A supply-chain network for transportation and logistics.

A supply-chain network for transportation and logistics.