Armann Ingolfsson

Availability

By appointment

About

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 , the Journal for Quantitative Analysis in Sports , and Health Care Management Science, and a Special Issue Guest Editor for Operations Research. 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.

Research

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

Teaching

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 421 - Data Visualization

Visual displays of quantitative information include charts, tables, maps, dashboards, animations, and more. Such displays can be used to understand, to inform, and to convince. This course will focus on strategies for carefully and clearly communicating analytical findings to the people who need to take action based on them. We will learn to use both basic tools (MS Excel) and advanced tools (Tableau and R) to create visual displays. Evaluation components will include assignments, presentations, and exams (midterm and final exam). Prerequisites: MGTSC 312.

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.

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