Thomas Knowles

  • Professor Emeritus of Management Science and Operations Management

Thomas W. Knowles is a Professor Emeritus of Management Science and Operations Management at IIT Stuart School of Business. Prior to his academic career, he worked for Lever Brothers Company as a plant engineer and production supervisor. He first joined IIT in the Department of Industrial and Systems Engineering, and moved to the business school in 1979.

Knowles is the author of the textbook Management Science: Building and Using Models, published by Richard D. Irwin, Inc. He has supervised many Ph.D. theses at IIT and served as a resource and committee member for many Ph.D. students from many of IIT's engineering and science departments. He has published numerous basic and applied research papers and presented his research at conferences in the U.S. and at international forums. He has held visiting appointments at the University of Chicago Booth School of Business.

He has consulted for many companies and government agencies, usually developing optimization-based decision models, in a wide range of business areas including operations, finance, and marketing. Examples include multi-level manufacturing planning for a large, multinational company; sizing and optimizing the operation of storage, pipeline, and purchase contracts for a natural gas company; developing a system to buy and sell bonds from a portfolio so that its characteristics track a selected index; scheduling ground personnel for an airline; allocating advertising funds to different media (considering cannibalization or halo effects on similar products from the same company); and many others.

Education

Ph.D., University of Chicago Booth School of Business, Management Science
M.B.A., University of Chicago Booth School of Business, Mathematical Methods and Computers
B.S., Purdue University, Chemical Engineering

Research Interests

Management Science/Operations Research
Mathematical Programming and Its Application
Spreadsheet Modeling
Scheduling and Production Planning and Control
Forecasting
Generalized Column Generation Methods