Michael Kay

Director of IMSEI

  • Phone: 919.515.2008
  • Office: 4179 Fitts-Woolard Hall

Michael G. Kay has been a professor of Industrial Engineering at North Carolina State University since 1992. He is the incoming Director of the Integrated Manufacturing Systems Institute and Interim Director of the Operations Research Graduate Program. He is the current Past-President of the College-Industry Council on Material Handling Education.

ISE 453: Design of Production, Logistics, and Service Systems
OR/ISE 501: Introduction to Operations Research
ISE 754: Logistics Engineering

Home Delivery Logistics Networks using Driverless Delivery Vehicles
Public Logistics Networks
Multimedia Sensor Fusion for Intelligent Camera Control

Matlog: Logistics Engineering Matlab Toolbox
Lgpy: Logistics Engineering Python Package
Julog: Logistics Engineering Julia Toolbox
GAOT: Genetic Algorithm Optimization Toolbox (zip)

Basic Concepts in Matlab (pdf)
Lecture Notes for Production System Design (pdf)
Material Handling Equipment (pdf)
Material Handling Equipment Taxonomy
Warehousing (pdf)

Research Interests

Michael G. Kay’s main research focus is on the design of public logistics networks. Areas of research interest include logistics network design. metaheuristics, freight transportation, material handling, warehousing, facilities design, and genetic algorithms.


Ph.D.Industrial EngineeringNC State University1992
MSIndustrial EngineeringUniversity of Florida1984
BAEconomicsUniversity of Florida1981

Discover more about Michael Kay



Challenges and opportunities to integrate the oldest and newest manufacturing processes: metal casting and additive manufacturing
Lynch, P., Hasbrouck, C., Wilck, J., Kay, M., & Manogharan, G. (2020), RAPID PROTOTYPING JOURNAL. https://doi.org/10.1108/RPJ-10-2019-0277
Digital Facility Layout Planning
Peron, M., Fragapane, G., Sgarbossa, F., & Kay, M. (2020), Sustainability. https://doi.org/10.3390/su12083349
Modeling and transportation planning for US noncombatant evacuation operations in South Korea
Kearby, J. A., Winz, R. D., Hodgson, T. J., Kay, M. G., King, R. E., & McConnell, B. M. (2020), Journal of Defense Analytics and Logistics, 4(1), 41–69. https://doi.org/10.1108/JDAL-05-2019-0010
Rethinking reverse logistics: role of additive manufacturing technology in metal remanufacturing
Strong, D., Kay, M., Wakefield, T., Sirichakwal, I., Conner, B., & Manogharan, G. (2020), JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 31(1), 124–144. https://doi.org/10.1108/JMTM-04-2018-0119
Assessing uncertainty and risk in an expeditionary military logistics network
McConnell, B. M., Hodgson, T. J., Kay, M. G., King, R. E., Liu, Y., Parlier, G. H., … Wilson, J. R. (2019), Journal of Defense Modeling and Simulation. https://doi.org/10.1177/1548512919860595
Hybrid manufacturing—Locating AM hubs using a two-stage facility location approach
Strong, D., Kay, M., Conner, B., Wakefield, T., & Manogharan, G. (2019), Additive Manufacturing, 25, 469–476. https://doi.org/10.1016/j.addma.2018.11.027
Route planning methods for a modular warehouse system
Dayıoğlu, E. G., Karagül, K., Şahin, Y., & Kay, M. G. (2019), An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 10(1), 17. https://doi.org/10.11121/ijocta.01.2020.00752
Time series anomaly detection from a markov chain perspective
Vasheghani Farahani, I., Chien, A., King, R. E., Kay, M. G., & Klenz, B. (2019), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 1000–1007. https://doi.org/10.1109/ICMLA.2019.00170
A Military Logistics Network Planning System
Rogers, M. B., McConnell, B. M., Hodgson, T. J., Kay, M. G., King, R. E., Parlier, G., & Thoney Barletta, K. (2018), Military Operations Research, 23(4), 5–24. https://doi.org/10.5711/1082598323405
A new method for generating initial solutions of capacitated vehicle routing problems
Karagul, K., Kay, M. G., & Tokat, S. (2018), Gazi University Journal of Science, 31(2), 489–513.

View all publications via NC State Libraries

Michael G. Kay