Sarah Ryan

Sarah Ryan

Joseph Walkup Professor

Research Interests

Dr. Ryan’s research examines the planning and operation of manufacturing and service systems under uncertainty. Currently, she is focusing on electric power systems and assembly systems. Issues under study by her research group include short term power system scheduling to accommodate renewable generation; analysis of the impact of gas system uncertainty on power system operations; and assessment of input reliability and solution quality in stochastic programming. Her work has been supported by the National Science Foundation, including a CAREER award, an AT&T Industrial Ecology Faculty Fellowship, the Department of Energy, and electric power systems consortia.

Recent Publications

  1. Dinakar Gade, Gabriel Hackebeil, Sarah M. Ryan, Jean-Paul Watson, Roger J-B Wets, and David L. Woodruff, Obtaining Lower Bounds from the Progressive Hedging Algorithm for Stochastic Mixed-Integer Programs, Mathematical Programming 157(1), 47-57 (2016). DOI: 10.1007/s10107-016-1000-z
  2. Yonghan Feng and Sarah M. Ryan, Day-Ahead Hourly Electricity Load Modeling by Functional Regression, Applied Energy, 170, 455-465 (2016) DOI: 10.1016/j.apenergy.2016.02.118
  3. Keyvanshokooh ,E. S. M. Ryan and E. Kabir, “Hybrid Robust and Stochastic Optimization for Closed-Loop Supply Chain Network Design using Accelerated Benders Decomposition,” European Journal of Operational Research, 249(1), 76-92 (2016).  DOI: 10.1016/j.ejor.2015.08.028
  4. Sari, D., Y. Lee, S. M. Ryan and D. L. Woodruff, “Statistical Metrics for Assessing the Quality of Wind Power Scenarios for Stochastic Unit Commitment,” Wind Energy 19(5), 873-893 (2016). DOI: 10.1002/we.1872
  5. Feng, Y. and S. M. Ryan, “Solution Sensitivity-Based Scenario Reduction for Stochastic Unit Commitment,” Computational Management Science, 13(1), 29-62 (2016).  DOI: 10.1007/s10287-014-0220-z
  6. Guo, G., G. Hackebiel, S. M. Ryan, J-P Watson, and D. L. Woodruff, “Integration of Progressive Hedging and Dual Decomposition in Stochastic Integer Programs,” Operations Research Letters 43(3), 311-316 (2015). DOI: 10.1016/j.orl.2015.03.008
  7. Feng, Y., I. Rios, S. Ryan, K. Spurkel, J-P Watson, R. Wets, and D. Woodruff. “Toward Scalable Stochastic Unit Commitment – Part I: Load Scenario Generation,” Energy Systems (2015). DOI: 10.1007/s12667-015-0146-8
  8. Cheung, K., D. Gade, C. Silva-Monroy, S. Ryan, J-P Watson, R. Wets, and D. Woodruff. “Toward Scalable Stochastic Unit Commitment – Part II: Assessing Solver Performance,” Energy Systems (2015). DOI: 10.1007/s12667-015-0148-6
  9. Jin, S., A. Botterud and S. M. Ryan, “Temporal vs. Stochastic Granularity in Thermal Generation Capacity Planning with Wind Power,” IEEE Transactions on Power Systems 29(5), 2033-2041 (2014). DOI: 10.1109/TPWRS.2014.2299760
  10. Gao, N. and S. M. Ryan, “Robust Design of a Closed-Loop Supply Chain for Uncertain Carbon Regulations and Random Product Flows,” EURO Journal on Transportation and Logistics 3(1), 5-34 (2014). DOI: 10.1007/s13676-014-0043-7

IMSE Courses Taught

IE 305 Engineering Economic Analysis
IE 413 Stochastic Modeling, Analysis and Simulation
IE 510 Network Analysis
IE 513 Analysis of Stochastic Systems
IE 633 Stochastic Programming



Office: 3017 Black Engineering
Phone: 515-294-4347
Fax: 515-294-3524


The University of Virginia, B.S. with High Distinction in Systems Engineering, 1983

The University of Michigan, M.S.E., Industrial and Operations Engineering, 1984

The University of Michigan, Ph.D., Industrial and Operations Engineering, 1988

Curriculum Vitae