• Department Head, Industrial & Systems Engineering
  • Professor, Industrial & Systems Engineering
  • Sugar and Mike Barnes Department Head Chair, Industrial & Systems Engineering
  • Phone: 979-845-5535
  • FAX: 979-458-4299
  • Email: Ntaimo@tamu.edu
  • Office: ETB 4029
Google Scholar Profile
Lewis Ntaimo

Educational Background

  • Ph.D., Systems & Industrial Engineering (Minor: Electrical & Computer Engineering), University of Arizona – 2004
  • M.S., Mining & Geological Engineering, University of Arizona – 2000
  • B.S., Mining Engineering, University of Arizona, Tucson – 1998

Research Interests

    • Optimization methods: dynamic, Integer, nonlinear, and stochastic programming
    • Modeling and design: systems thinking, modeling and simulation, engineering processes,
      facilities planning
    • Applications: epidemics, healthcare, manufacturing, mining, wildfire management, etc.

Certifications & Memberships

  • Engineer-In-Training (E.I.T), State of Arizona – 1999
  • Fellow, Institute of Industrial and Systems Engineers (IISE) – 2024

Awards & Honors

  • Distinguished Educator Award, Industrial Engineering and Operations Management (IEOM) Society International – 2022
  • Certificate of Appreciation for serving on the Institute for Operations Research and the Management Sciences (INFORMS) Conference Committee – 2021
  • Outstanding Faculty Award, Texas A&M University INFORMS Student Chapter – 2019
  • Certificate of Appreciation for being INFORMS Tutorials Chair – 2018
  • First place, INFORMS Minority Issues Forum Paper Competition – 2017
  • Best Publication Award in Natural Resources, INFORMS Section on Energy, Natural Resources, and the Environment – 2016
  • Second place in paper competition, INFORMS Minority Issues Forum
  • Marilyn and L. David Black Faculty Fellow in Industrial and Systems Engineering, Texas A&M University – 2016
  • Top 5 Papers Published in Journal of Global Optimization in 2013: Ntaimo, L., “Fenchel decomposition for stochastic mixed-integer programming,” Vol. 55, pages 141-163 – 2014
  • INFORMS Computing Society (ICS) Prize awarded for seminal work on stochastic mixed-integer programming – 2015

Selected Publications

  • Computational Stochastic Programming: Models, Algorithms and Implementation, Springer International Publishing, DOI: 10.1007/978-3-031-52464-6: ISBNs: 978-3-03- 152462-2, 978-3-03-152464-6, April 2024.
  • Gong, J.*, K.R. Gujjula and L. Ntaimo, “An Integrated Chance Constraints Approach for Optimal Vaccination Policies Under Uncertainty for COVID-19,” Socioeconomic Planning Science, 2023. Jun;87:101547. doi: 10.1016/j.seps.2023.101547. Epub 2023 Feb 21. PMID: 36845344; PMCID: PMC9942454.
  • Venkatachalam, S.* and L. Ntaimo, “Integer Set Reduction Algorithm for Stochastic Mixed-Integer Programming,” Computational Optimization and Applications, Vol. 85, No. 1, pp. 181-211, 2023.
  • Gujjula, K.R., J. Gong*, B. Segundo*, and L. Ntaimo, “COVID-19 Vaccination Policies Under Uncertain Transmission Characteristics Using Stochastic Programming,” PLoS One, Vol. 17, No. 4, 2022.
  • Piazza, A., B.K. Pagnoncelli and L. Ntaimo, “What is the Optimal Cutoff Surface for Ore Bodies with More than One Mineral?”, Operations Research Letters, Vol. 50, No. 2, pp. 137-144, 2022.
  • Rhodes, N.*, L. Ntaimo and L. Roald, “Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs,” IEEE Transactions on Power Systems, Vol. 36, No. 4, pp. 3118-3128, 2021.
  • Prakash, A.*, V. Panchang, Y. Ding and L. Ntaimo, “Sign Constrained Bayesian Inference for Nonstationary Models of Extreme Events,” Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 146, No. 5, 2020.
  • Canessa, G.*, J.A. Gallego*, L. Ntaimo, B.K. Pagnoncelli “An algorithm for Binary Chance-Constrained Problems Using IIS,” Computational Optimization and Applications, Vol. 72, No. 3, pp. 589-608, 2019.
  • Alvarado, M.M. and L. Ntaimo, “Chemotherapy Appointment Scheduling Under Uncertainty Using Mean-Risk Stochastic Integer Programming,” Health Care Management Science, Vol. 21, No. 1, pp. 87-104, 2018.
  • Cotton, T. and L. Ntaimo. “Computational Study of Mean-Risk Stochastic Linear Programs,” Mathematical Programming Computation, Vol. 7, pp. 471-499, 2015.
Google Scholar Profile