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David L. Woodruff
Researcher at University of California, Davis
Publications - 127
Citations - 6719
David L. Woodruff is an academic researcher from University of California, Davis. The author has contributed to research in topics: Stochastic programming & Tabu search. The author has an hindex of 36, co-authored 124 publications receiving 5800 citations. Previous affiliations of David L. Woodruff include University of California & University of Colorado Boulder.
Papers
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Journal ArticleDOI
CONWIP: a pull alternative to kanban
TL;DR: In this paper, a pull-based production system called CONWIP is described and theoretical arguments in favour of the system are outlined and simulation studies are included to give insight into the system's performance.
Journal ArticleDOI
Pyomo: modeling and solving mathematical programs in Python
TL;DR: Pyomo provides a capability that is commonly associated with algebraic modeling languages such as AMPL, AIMMS, and GAMS, but Pyomo’s modeling objects are embedded within a full-featured high-level programming language with a rich set of supporting libraries.
Book
Pyomo - Optimization Modeling in Python
TL;DR: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners.
Journal ArticleDOI
Identification of Outliers in Multivariate Data
David M. Rocke,David L. Woodruff +1 more
TL;DR: The question of what levels of contamination can be detected by this algorithm as a function of dimension, computation time, sample size, contamination fraction, and distance of the contamination from the main body of data is investigated.
Journal ArticleDOI
Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems
TL;DR: The necessity and efficacy of the techniques is empirically assessed on a two-stage stochastic network flow problem with integer variables in both stages and algorithmic innovations in the context of a broad class of scenario-based resource allocation problem.