What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Mathematical optimization
His scientific interests lie mostly in Mathematical optimization, Software, Tabu search, Stochastic optimization and Integer.
His Mathematical optimization research is multidisciplinary, incorporating elements of Algorithm, Multivariate normal distribution and Outlier.
His research integrates issues of Anomaly detection, Data mining, Identification and Sample size determination in his study of Software.
David L. Woodruff combines topics linked to Operations research with his work on Tabu search.
His Stochastic optimization study incorporates themes from Class and Flow network.
His Integer research incorporates elements of Key and Integer programming.
His most cited work include:
- CONWIP: a pull alternative to kanban (769 citations)
- Pyomo: modeling and solving mathematical programs in Python (323 citations)
- Identification of Outliers in Multivariate Data (276 citations)
What are the main themes of his work throughout his whole career to date?
David L. Woodruff mainly focuses on Mathematical optimization, Operations research, Stochastic programming, Tabu search and Algorithm.
His studies deal with areas such as Decision support system and Integer as well as Mathematical optimization.
His Operations research research includes elements of Routing, Scheduling, Operations management and Production planning.
His Stochastic programming research is multidisciplinary, relying on both Python, Stochastic optimization and Theory of computation.
David L. Woodruff usually deals with Tabu search and limits it to topics linked to Metaheuristic and Local search, Beam search, Theoretical computer science and Heuristics.
David L. Woodruff combines subjects such as Covariance and Estimator with his study of Algorithm.
He most often published in these fields:
- Mathematical optimization (37.14%)
- Operations research (18.57%)
- Stochastic programming (14.29%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (37.14%)
- Probabilistic logic (6.43%)
- Power system simulation (7.86%)
In recent papers he was focusing on the following fields of study:
David L. Woodruff spends much of his time researching Mathematical optimization, Probabilistic logic, Power system simulation, Operations research and Stochastic programming.
While working in this field, David L. Woodruff studies both Mathematical optimization and Sequential quadratic programming.
His Probabilistic logic study also includes fields such as
- Optimization problem that connect with fields like Machine learning, Data mining, Sample, Benchmark and Artificial intelligence,
- Econometrics which is related to area like Dimension.
The Operations research study combines topics in areas such as Theory of computation and Management science.
He interconnects Search tree, Heuristic and Branch and bound in the investigation of issues within Integer.
His study looks at the intersection of Algorithm and topics like Work in process with Python and Software.
Between 2014 and 2021, his most popular works were:
- Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs (93 citations)
- Toward scalable stochastic unit commitment. Part 2: Solver Configuration and Performance Assessment (40 citations)
- The stochastic vehicle routing problem, a literature review, part I: models (32 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical optimization
- Algorithm
His primary areas of investigation include Power system simulation, Mathematical optimization, Probabilistic logic, Stochastic programming and Stochastic process.
His Mathematical optimization study combines topics in areas such as Baseline and Integer.
His Probabilistic logic research focuses on Econometrics and how it connects with Autocorrelation, Brier score and Estimator.
His Stochastic programming research is multidisciplinary, incorporating perspectives in Sampling, Construct, Artificial intelligence, Benchmark and Sample.
The various areas that David L. Woodruff examines in his Stochastic process study include Routing, Vehicle routing problem and Operations research.
His Scalability study integrates concerns from other disciplines, such as Solver, Decomposition and Component.
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