Sarah Ryan


C.G. “Turk” and Joyce A. Therkildsen Department Chair, Professor
Industrial & Manufacturing Systems Engineering
Director, DataFEWSion Graduate Traineeship


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 supply chain design for distributed manufacturing and renewable energy integration. Methodological issues under study by her research group include  the 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


  • Wellington Award, Engineering Economy Division of the Institute of Industrial & Systems Engineers, 2021
  • D. R. Boylan Eminent Faculty Award for Research, Iowa State University College of Engineering, 2017
  • Fellow, Institute of Industrial & Systems Engineers, 2013


  • Görkem Emirhüseyinoğlu and Sarah M. Ryan, “Farm management optimization under uncertainty with impacts on water quality and economic risk,” IISE Transactions 54(12): 1143-1160 (2022). DOI: 10.1080/24725854.2022.2031351 (featured in ISE Magazine, Nov 2022)

  • Ge Guo and Sarah M. Ryan, “Sequencing Mixed-Model Assembly Lines with Risk-Averse Stochastic Mixed-Integer Programming,” International Journal of Production Research 60(12): 3774-3791 (2022). DOI: 10.1080/00207543.2021.1931978.
  • Xiaoshi Guo and Sarah M. Ryan, “Portfolio rebalancing based on time series momentum and downside risk,” IMA Journal of Management Mathematics (2021). DOI: 10.1093/imaman/dpab037

  • 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.
  • 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