Sarah Ryan image

Sarah Ryan


Interim Department Chair and Joseph Walkup Professor
Industrial & Manufacturing Systems Engineering
Director, DataFEWSion Graduate Traineeship
Editor-in-Chief, The Engineering Economist


3004/3017 Black Engr
2529 Union Dr
Ames, IA 50011-2030



  • PhD, Industrial and Operations Engineering, The University of Michigan
  • MSE, Industrial and Operations Engineering, The University of Michigan
  • BS (with high distinction), Systems Engineering, The University of Virginia

Interest Areas

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.


  • Xiaoshi Guo and Sarah M. Ryan. Reliability assessment of scenarios generated for stock index returns incorporating momentum. International Journal of Finance & Economics (2020). DOI: 10.1002/ijfe.2002.
  • Dan Hu and Sarah M. Ryan. Quantifying the effect of uncertainty in the gas spot price on power system dispatch costs with estimated correlated uncertainties. Energy Systems 11: 859-884 (2020). DOI: 10.1007/s12667-019-00358-8.
  • Görkem Emirhuseyinoglu and Sarah M. Ryan. Land use optimization for nutrient reduction under stochastic precipitation rates. Environmental Modelling and Software 123 (2020). DOI: 10.1016/j.envsoft.2019.104527.
  • Narges Kazemzadeh, Sarah M. Ryan and Mahdi Hamzeei, “Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation,” Energy Systems 10(3): 517-541 (2019). DOI: 10.1007/s12667-017-0265-5.
  • Didem Sarı and Sarah M. Ryan, Observational data-based quality assessment of scenario generation for stochastic programs. Computational Management Science (2018). DOI: 10.1007/s10287-019-00349-1
  • Ali Haddadsisakht and Sarah M. Ryan, “Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax,” International Journal of Production Economics 195, 118-131 (2018). DOI: /10.1016/j.ijpe.2017.09.009.
  • 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
  • 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
  • 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
  • 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