Rate My Professor Siggi Olafsson

Sigurdur “Siggi” Olafsson serves as Associate Professor in the Department of Industrial and Manufacturing Systems Engineering at Iowa State University of Science and Technology, a position held since his promotion in 2004 following an initial appointment as Assistant Professor in 1998. He earned a Ph.D. in Industrial Engineering from the University of Wisconsin-Madison in 1998, an M.S. in Industrial Engineering from the same institution in 1996, and a B.S. in Mathematics from the University of Iceland, Reykjavik, in 1994. Olafsson’s research focuses on operations research and data analytics, with expertise in heuristic methods for discrete optimization and predictive plant phenotyping, as well as data-driven production scheduling, data mining of incident reporting databases, and sports analytics. Key projects include decision support for plant breeding, expert systems to improve data collection from law enforcement, and predicting the hardness of new materials.

Olafsson co-authored the book Nested Partitions Optimization: Methodology and Applications, published by Springer in 2008, and has produced numerous peer-reviewed journal articles. Recent publications encompass “Insights from Dynamic Pricing Scenarios for Multiple-generation Product Lines with an Agent-based Model using Text Mining and Sentiment Analysis” (2024, International Journal of Advances in Production Research), “Identifying Critical Drivers of Mango, Tomato, and Maize Postharvest Losses (PHL) in Low-Income Countries and Predicting Their Impact” (2023, Agriculture), “A Regression Approach to Identify Discriminating Locations” (2023, Crop Science), “Crop Phenotype Prediction using B-clustering to Explain Genotype-by-Environment Interactions” (2022, Frontiers in Plant Science), and “Biclustering with Missing Data” (2020, Information Sciences). Earlier works include “Discovering Dispatching Rules Using Data Mining” (2005, Journal on Scheduling), “Stochastic Flow Shop Scheduling Model for the Panama Canal” (2011, Journal of the Operational Research Society), and “Operations Research and Data Mining” (2008, European Journal of Operational Research). He received best poster awards in 2018 at the First Midwest Statistical Machine Learning Colloquium and the IMSE Research Symposium for bi-clustering research. Olafsson’s scholarship bridges optimization and machine learning with applications in manufacturing, agriculture, and public safety.