Directory

Sarah Ryan

  • Joseph Walkup Professor
  • Industrial & Manufacturing Systems Engineering

Main Office

3017 Black Engr
Ames, IA 50011-2164
Phone: 515-294-4347
Fax: 515-294-3524




Education

  • PhD, Industrial and Operations Engineering, University of Michigan, 1988
  • MSE, Industrial and Operations Engineering, University of Michigan, 1984
  • BS (with high distinction), Systems Engineering, University of Virginia, 1983

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.

Selected Publications

  • 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.
  • 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, published online 2017. DOI: 10.1007/s12667-017-0265-5.
  • Didem Sarı and Sarah M. Ryan, “Reliability of wind power scenarios and stochastic unit commitment cost,” Energy Systems published online 2017. DOI: 10.1007/s12667-017-0255-7.
  • 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
  • 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
  • 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
  • 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