David Woodruff
Professor of Management
Research Expertise: Operations and management science, planning and scheduling under uncertainty, optimization
David L. Woodruff is Professor at the Graduate School of Management at the University of California at Davis. His research concerns computational aspects of optimal decision making. He is particularly interested in problems with a mix of discrete and continuous choices with multiple time stages when there is significant uncertainty. He research includes solution algorithms, problem representation and modeling language support. He has worked on applications in operations, logistics, science, and has been involved recently in a number of applications in electrical energy planning and scheduling.
He is Editor-in-Chief of the INFORMS Journal on Computing, which is a publication of the Institute for Operations Research and Management Science. His paper with Jean-Paul Watson entitled “Progressive Hedging Innovations for a Class of Stochastic Mixed-integer Resource Allocation Problems” shared the best paper of 2011 award in the journal Computational Management Science. He is the UCD Principal Investigator for the ARPAe (Department of Energy) grant “Improved Power System Operation Using Advanced Stochastic Optimization” awarded in 2012.
Professor Woodruff teaches the core class in Managing for Operational Excellence. He has also teaches the Management Science class from time to time. Woodruff has served a term as Associate Dean for Instructional Programs and also as Director of Concurrent Degree Programs.
Room 3108G
Planning for Uncertainty in Power Generation
Renewable sources such as wind and solar power are an increasing part of the nation’s energy mix, but these green resources also bring new uncertainty to our power supply. Professor David Woodruff is collaborating on a new, federally funded project to help power utilities navigate in this new reality.
“The goal is to be able to plan to generate power in the face of the uncertainty caused by a 30 percent penetration of renewables in the power supply,” said Woodruff.
Coordinating Traditional and Renewable Energy Sources
Mathematicians Address Complex Issues in Electricity Supply
Professor David Woodruff was one of two UC Davis professors recently awarded grant funding from the U.S. Department of Energy to research optimal ways to integrate traditional energy sources with new renewable energy sources. This article describes Woodruff’s contributions to the Green Electricity Network Integration (GENI) project.
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
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.