David Woodruff
Professor of Management
Research Expertise: Operations and management science, planning and scheduling under uncertainty, optimization
Professor David Woodruff explores operations management of companies and organizations, including capacity planning, scheduling, supply chain optimization, materials management, inventory control, quality control and distribution. He is an expert in optimization and planning under uncertainty. He teaches courses in production and operations management and optimal decision making.
Woodruff recently co-authored a book that provides managers and information technology professionals with a basic understanding of computational optimization models for production planning in a supply chain. He has published numerous articles in leading academic journals and has served on the editorial boards of the International Journal of Production Research, Production and Operations Management and Naval Research Logistics. He is past chair of the INFORMS Computing Society, which is concerned with computer science, artificial intelligence, and their relationship to operations research and the management sciences. He has served as area editor of the Journal of Heuristics and currently serves as area editor for the INFORMS Journal On Computing.
As director of concurrent degree programs, Professor Woodruff oversees the School’s interdisciplinary studies, including MBA/Law, MBA/Engineering, MBA/MD and other MBA joint programs, as well as programs that enable graduate researchers and Ph.D. candidates in the sciences to concurrently earn an MBA.
Room 3108G
PySP: Modeling and Solving Stochastic Programs in Python
Mathematical Programming Computation, 2012
Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its wide-spread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case.
Pyomo – Optimization Modeling in Python
Springer, 2012
This book by Professor David Woodruff and co-authors William Hart and Jean-Paul Watson from Sandia National Laboratories and Carl Laird from Texas A&M provides a complete and comprehensive guide to Pyomo—Python Optimization Modeling Objects—for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners.
Introduction to Computational Optimization Models for Production Planning in a Supply Chain, 2nd Edition
Springer, 2006
The book by Professor David Woorduff and co-author Stefan Voß from the University of Hamburg begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation, the book develops fairly sophisticated models for supply chain management.
David Woodruff Awards
Optimizing the Next-Generation Power Grid
Amid increasing demand and more renewables-generated power coming online, government policy makers, utility firms and infrastructure companies worldwide need more and better information to plan for future energy generation and transmission uncertainties.
206 Decision Making and Management Science
Considers management science for decision makers. Topics include an introduction to modeling and decision analysis, an introduction to optimization and linear programming, modeling and solving linear programming problems in a spread sheet, sensitivity analysis and the simplex method, networks, integer linear programming, project management and decision analysis.
Modeling and Solving a Large-scale Generation Expansion Planning Problem under Uncertainty
Energy Systems, 2011
In this paper, Professor David Woodruff and co-authors Shan Jin and Sarah Ryan from Iowa State University, and Jean-Paul Watson from Sandia National Laboratories formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices.
Pyomo: Modeling and Solving Mathematical Programs in Python
Mathematical Programming Computation, 2011
In this study, Professor David Woodruff and co-authors William E. Hart and Jean-Paul Watson from Sandia National Laboratories describe Pyomo, an open source software package for modeling and solving mathematical programs in Python. Pyomo can be used to define abstract and concrete problems, create problem instances, and solve these instances with standard open-source and commercial solvers.
Research Note: the Point of Diminishing Returns in Heuristic Search
International Journal of Metaheuristics, 2011
In this paper, Professor David Woodruff and co-authors Ulrike Ritzinger from Vienna University of Technology and Johan Oppen from Molde University College provide a computable definition for the intuitive concept of the point of diminishing returns in a heuristic search. The authors also demonstrate that with proper scaling, the time point for a small instance can provide some guidance concerning the time point on larger instances. This paper presents computational results for a range of problems and search methods.
Progressive Hedging Innovations for a Class of Stochastic Mixed-integer Resource Allocation Problems
Computational Management Science, 2011
Numerous planning problems can be formulated as multi-stage stochastic programs and many possess key discrete (integer) decision variables in one or more of the stages. Progressive hedging (PH) is a scenario-based decomposition technique that can be leveraged to solve such problems.