MétaCan
Menu
Back to cohort
Record W4211052544 · doi:10.1287/inte.1100.0522

Contributors

2010· article· en· W4211052544 on OpenAlexaboutno aff

Bibliographic record

VenueINFORMS Journal on Applied Analytics · 2010
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessComputer science

Abstract

fetched live from OpenAlex

Hernán Abeledo is an associate professor at the Department of Engineering Management and Systems Engineering of the George Washington University, where he teaches courses in operations research and mathematical optimization. His research interests are mainly in the fields of combinatorial optimization and integer programming. He has worked on optimization problems in telecommunication networks, workforce and operations scheduling, mathematical chemistry, game theory, and medical treatment planning. He holds an undergraduate degree in mathematics from the University of Buenos Aires and a PhD in operations research from Rutgers University. Mehmet O. Atan received his PhD in industrial and systems engineering at Lehigh University in May 2010. He had been a member of the Center for Value Chain Research at Lehigh and conducted research on planning problems in the high-tech industry. In 2008, he completed an internship with the Decision Technologies Group at Intel Corporation in Chandler, Arizona. Berrin Aytac is a visiting research scientist at Lehigh University. She holds a PhD in industrial engineering and an MS in management science from Lehigh. Her research interests include operations management and logistics, game theory, and supply chain optimization. Srinivas Bollapragada is a Principal Scientist at General Electric's (GE) Global Research Center in Niskayuna, New York. His research interests are in business applications of optimization technologies. During the past 15 years, he led the development of numerous innovative, algorithms-based business systems that are currently in use at GE's rail, energy, financial, health-care, and NBC Universal businesses, and at Norfolk Southern Railroad. He earned a PhD in operations management from Carnegie Mellon University and has published over 20 articles in leading operations research journals. His contributions were recognized by a number of awards, including the Blodgett award for technical excellence at GE. He was named an INFORMS Fellow in 2009. Steven M. Brown is an industrial engineer in IBM's 300 mm manufacturing facility and a member of the development team for EPOS. He is a member of INFORMS. He received an MBA from the University of North Carolina at Chapel Hill and a BS in operations research from Cornell University. His professional interests focus on the introduction of new technologies for operations and supply chain planning. Nelci Carvalho do Nascimento worked at Petrobras for over 20 years. He was supervisor of the helicopter airport at Macaé and now works as a consultant. Jeff Day earned a PhD in operations research from the Georgia Institute of Technology. During his professional career, he has worked for a railroad, a trucking company, and a consulting firm. He currently works in the SAS Center of Excellence for OR Applications. Abraham George is an operations research analyst at AT&T Shannon Laboratories. He holds a PhD in operations research and financial engineering from Princeton University. Prior to his current position, he was a postdoctoral fellow in CASTLE Laboratory at Princeton University. Ted Gifford is a Distinguished Member of Technical Staff at Schneider National Inc., where he leads strategic development initiatives. In his most recent prior position, he served as vice president of engineering and research, where he led an organization engaged in supply chain and transportation engineering, industrial engineering, statistical analysis, operations research modeling, business consulting, and development of decision support software tools. Michael F. Gorman is an associate professor and J. Robert Berry Endowed Fellow at the University of Dayton, and president of MFG Consulting after a 10-year rail career at BNSF Railway. He has been a finalist for the Edelman Award once and the Wagner Award twice. He has served as treasurer, secretary, vice chair, and chair for the Rail Applications Section (RAS) of INFORMS, and secretary of CPMS. He earned a PhD at Indiana University in 1994. Thomas Hanschke is president of Clausthal University of Technology and holds the Chair for Stochastic Models in Engineering Science in the university's Institute of Mathematics. He received a diploma degree in mathematics and a PhD in mathematics from the Karlsruhe University, Germany. Prior to joining Clausthal University of Technology, he was a senior technical staff member at IBM. His research focuses on performance analysis of complex manufacturing and air traffic systems. Karl G. Kempf is the director of decision engineering at Intel Corporation and manager of the team at Intel that won the 2009 INFORMS Prize. He is a member of the National Academy of Engineering and an adjunct professor at Arizona State University. He holds degrees in physics, chemistry, applied mathematics, and computer science. Ingo Meents develops storage systems as an accredited IT architect for IBM Germany Research and Development. His current focus is on clustered network attached storage systems. He started his work in the field of operations research with simulation, modeling, and optimization of material flows in the semiconductor industry. He received a diploma and a PhD degree in computer science from Clausthal University of Technology, Germany. He is a member of the German Operations Research Society. Fernanda Menezes is an account manager at GAPSO Tecnologia da Decisão, an operations research consulting firm in Rio de Janeiro, Brazil. She holds an MS degree in electrical engineering from the Pontificia Universidade Católica do Rio de Janeiro. Her research interests lie mainly in the application of combinatorial optimization techniques to logistics and production planning problems. Asima Mishra is a senior operations research engineer in the Supply Chain Modeling and Solutions Group of Intel, Santa Clara, where she has been developing and implementing forecasting and inventory models for high-technology products with short life cycles and rapid product transitions. She holds a PhD in operations management and an MS in industrial engineering from Purdue University. Lorenza Moreno is a researcher at the Algorithms and Decision Technology Lab at the Pontificia Universidade Católica do Rio de Janeiro. She holds a DSci degree in computer science from the university. Her research interests lie mainly in routing and scheduling problems and in the application of combinatorial optimization techniques to logistics, telecommunications, and production planning problems. John Nienow has been with Schneider National Inc. since 1984. In that time he has held positions as a business analyst, market research analyst, pricing manager, and director of transportation engineering. He is senior engineer with Schneider's Corporate Research Group. Marcus Poggi de Aragão is an associate professor at the Pontificia Universidade Catolica do Rio de Janeiro. He holds a PhD in applied mathematics from Ecole Polytechnique de Montreal. He is recognized for his work on branch-cut-and-price algorithms and by the results obtained on routing problems. His main interests include heuristics, metaheuristics, and matheuristics for combinatorial optimization problems, and applications in logistics, telecommunications, and production planning. Oscar Porto is a founder and the business director of GAPSO Tecnologia da Decisão, an operations research consulting firm in Rio de Janeiro, Brazil. He holds a PhD degree in computing and systems engineering from the Federal University of Rio de Janeiro. He was an associate professor at the Department of Electrical Engineering of the Catholic University of Rio de Janeiro until 2007. His main interests include applications of combinatorial optimization techniques in logistics, telecommunications, and production planning problems. Warren B. Powell is a faculty member in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981. He is director of CASTLE Laboratory, which works with a number of large transportation companies to develop advanced operational planning models that capture both uncertainty and a high level of detail. He is the author of Approximate Dynamic Programming: Solving the Curses of Dimensionality and has published extensively on stochastic dynamic problems in transportation. He is an INFORMS Fellow and has held numerous service positions within INFORMS. Marcelo L. Reis is an optimization manager at GAPSO Tecnologia da Decisão, an operations research consulting firm in Rio de Janeiro, Brazil. He holds an MSc degree in computer science from the Pontificia Universidade Católica do Rio de Janeiro. His research interests lie mainly in the application of combinatorial optimization techniques to logistics, telecommunications, and production planning problems. Shamin A. Shirodkar manages the Solver Solution Engineering Team, which specializes in building algorithm-based decision support solutions that run Intel's supply chain. Over the years he has applied the theory of constraints to ramping semiconductor fabrication facilities and implemented a variety of decision science tools in the supply chain arena, both internal to Intel as well as collaboratively among Intel, its material suppliers, and manufacturing subcontractors. In addition, he has served as a factory supply planning manager for Intel's assembly test factory in Chengdu, China. Hugo P. Simão is a senior operations research engineer at CASTLE Laboratory in the Department of Operations Research and Financial Engineering at Princeton University. He ho

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.377
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6230.467

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.215
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueINFORMS Journal on Applied AnalyticsSame topicScheduling and Optimization AlgorithmsFrench-language works237,207