Armann Ingolfsson

Professor, Department of Accounting and Business Analytics


Professor, Department of Accounting and Business Analytics
(780) 492-7982
4-30K Business Building
11203 Saskatchewan Drive NW
Edmonton AB
T6G 2R6



Armann Ingolfsson is a Professor of Operations Management and the Academic Director of the Centre for Excellence in Operations at the University of Alberta School of Business. He received a PhD in operations research from MIT. His research interests focus on operations management in the service and health care sectors and developing methodology to analyze congested systems. He has published articles on these topics in Management Science, Operations Research, Production and Operations Management, and various other journals. He was the Editor-in-chief of INFORMS Transactions on Education from 2013 to 2015 and he is currently an Associate Editor for INFOR and for the Journal for Quantitative Analysis in Sports . He has taught operations management, analytics, and statistics to undergraduate, MBA, executive education, and PhD students. He is a past President of the Canadian Operational Research Society (CORS) and has served in various other roles for CORS.


My research program focuses on developing mathematical models and algorithms to predict and optimize the performance of production and service systems, with particular emphasis on services and health care. Services employ the majority of the workforce in most developed economies. Productivity, by most measures, is considerably lower in services than other sectors, so there are clear opportunities for performance improvement in services. The health care sector forms a large and growing portion of the service sector and it is a prime source of interesting and important planning problems. Health care planning is important not only in economic terms but also because improved planning could save lives or increase quality of life. My objectives are to develop tools that are useful to planners in making decisions about, for example, capacity planning or facility location. The models underlying the tools should be sufficiently realistic to be credible to practitioners and sufficiently accurate to distinguish between the consequences of different decisions, they should be tractable, and it should be possible to use them on typical desktop computers. At the same time, I strive to pose and solve problems that are interesting and challenging, and through doing so, contribute to the methodological basis of operations research


I teach courses on operations management, decision models, and data analysis. In my courses, I strive to create opportunities for students to learn to convert raw data into information that is useful for decision making. I focus on learning by doing because I believe that doing leads to the longest-lasting learning. The central skill that students in my courses learn is to analyze data and create and analyze models using spreadsheets. That is also the skill that I test on computer-based exams. I often use current events and real data to illustrate the relevance of operations management and decision modeling. Finally, I strive to be an instructor who is organized, available, and fair.


OM 352 - Operations Management

A problem-solving course which introduces the student to deterministic and stochastic models which are useful for production planning and operations management in business and government. Note: Students are expected to have basic familiarity with microcomputer applications. Prerequisite: MATH 114 or equivalent and STAT 151 or equivalent.

Winter Term 2021
OM 701 - Introduction to Operations Management Research

This course provides a general introduction to the major research fields of operations management (OM). The focus will be on reading and evaluating current papers from prominent OM journals. The theory of science and the review process will be briefly discussed. Students are expected to have as mathematical background the equivalent of an upper-level undergraduate or first-year graduate courses in optimization and probability or stochastic modeling. This course may be appropriate for some graduate students in engineering or computing science. Prerequisite: A graduate or undergraduate course in operations management. Open to all doctoral students or with the written permission of the instructor. Approval of the Business PhD Program Director is also required for non-PhD students.

Fall Term 2020

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