Methodologies: Stochastic Programming, Nonlinear Optimization, Integer Programming
Applications: Power Systems and Building Energy, Healthcare Systems, Shared Mobility

Yiling Zhang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. She received her Ph.D. in Industrial and Operations Engineering at the University of Michigan, Ann Arbor. Her research interests are in stochastic programming, integer programming, nonlinear programming. Applications include risk analysis of energy systems, healthcare operations, and transportation systems. Her research has been published in journals such as Operations Research, SIAM Journal on Optimization, and Manufacturing & Service Operations Management. Her work has been recognized by several awards, including Honorable Mention for INFORMS Optimization Society Student Paper Prize, the Richard & Eleanor Towner Prize for Distinguished Academic Achievement, and the Murty Prize for Best Student Paper in Optimization.

More information can be found on Zhang's personal website.

Education

Ph.D., Industrial and Operations Engineering, 2019
University of Michigan, Ann Arbor, USA

M.S., Industrial and Operations Engineering, 2015
University of Michigan, Ann Arbor, USA

B.Eng., Automation, 2013
Tsinghua University, Beijing, China

Scientific & Professional Societies

Honors and Awards

• Institute on the Environment Mini Grants, University of Minnesota, 2019
• Honorable Mention for INFORMS Optimization Society Student Paper Prize, 2019
• Richard F. and Eleanor A. Towner Prize for Distinguished Academic Achievement, University of Michigan, 2019

Selected Publications

Zhang, Y., Dong, Jin. (2022). Building Load Control using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants. To appear in INFORMS Journal on Computing.

Zhang, Y., Lu, M., Shen, S. (2019). On the values of vehicle-to-grid selling in electric vehicle sharing. Manufacturing and Service Operations Management, 23(2), 267-545.

Zhang, Y., Jiang, R., & Shen, S. (2018). Ambiguous chance-constrained binary programs under mean-covariance information. SIAM Journal on Optimization, 28(4), 2922-2944.

Zhang, Y., Shen, S., Erdogan, S. A. (2018). Solving 0-1 semidefinite programs for the distributionally robust allocation of surgery blocks. Optimization Letters, 12(7), 1503–1521.

Jiang, R., Shen, S., & Zhang, Y. (2017). Integer programming approaches for appointment scheduling with random no-shows and service durations. Operations Research, 65(6), 1638-1656.

Zhang, Y., Shen, S., & Erdogan, S. A. (2017). Distributionally robust appointment scheduling withmoment-based ambiguity set. Operations Research Letters, 45(2), 139-144.

Zhang, Y., Shen, S., & Mathieu, J. L. (2017). Distributionally robust chance-constrained optimalpower flow with uncertain renewables and uncertain reserves provided by loads. IEEE Transactions on Power Systems, 32(2), 1378-1388.