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Record W4229823162 · doi:10.1287/opre.1100.0901

Contributors

2010· article· en· W4229823162 on OpenAlexaboutno aff

Bibliographic record

VenueOperations Research · 2010
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsOperations researchComputer scienceGame theoryInfluence diagramMarkov decision processManagement scienceArtificial intelligenceMathematical economicsMathematicsEngineeringMarkov processDecision tree

Abstract

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Richa Agarwal (“ Network Design and Allocation Mechanisms for Carrier Alliances in Liner Shipping ”) is a research scientist at Amazon.com. She received her Ph.D. in algorithms, combinatorics, and optimization from Georgia Institute of Technology in 2007. Her research interests span integer programming, network optimization, combinatorial optimization, and algorithmic game theory. She has recently focused on design of large-scale networks and management of decentralized systems. Ravindra K. Ahuja (“ Fast Algorithms for Specially Structured Minimum Cost Flow Problems with Applications ”) is a professor in industrial and systems engineering at the University of Florida. He conducts research in the theory and application of network optimization and publishes widely in prestigious journals. He is a winner of the INFORMS 1993 Lanchester Prize, 2004 Pierskalla Award, 2006 Wagner Prize, and 2007 Koopman Award. He is an associate editor for the journals Operations Research, Transportation Science, and Networks. Oguzhan Alagoz (“ Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors ”) is an assistant professor of industrial and systems engineering at the University of Wisconsin–Madison. His research interests include medical decision making, completely and partially observable Markov decision processes, discrete-event system simulation, health-care applications, and risk-prediction models. Alfredo Altuzarra (“ Consensus Building in AHP-Group Decision Making: A Bayesian Approach ”) is an associate professor in statistics and operations research of the Faculty of Economics at the University of Zaragoza. He received his Ph.D. in mathematics in 2005. His current research interests include decision theory, Bayesian inference, analytic hierarchy process, and multicriteria decision making with applications to economics and business. Hamsa Balakrishnan (“ Algorithms for Scheduling Runway Operations Under Constrained Position Shifting ”) is the T. Wilson Career Development Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT). She received a B.Tech. in aerospace engineering from the Indian Institute of Technology, Madras, and a Ph.D. in aeronautics and astronautics from Stanford University. Prior to joining MIT, she was a researcher at the University of California, Santa Cruz, and the NASA Ames Research Center. She was the recipient of an NSF CAREER Award in 2008. Her research interests address various aspects of air transportation systems, including algorithms for air traffic scheduling and routing, air traffic surveillance algorithms, and mechanisms for the allocation of airport and airspace resources. Marco Antonio Boschetti (“ An Exact Algorithm for the Two-Dimensional Strip-Packing Problem ”) is a researcher at the Department of Mathematics of the University of Bologna, Italy. He received his Ph.D. in operational research from the Business School of Imperial College, London, in 1999, and joined the faculty of the University of Bologna in 2002. His main research interest is the development of exact and heuristic algorithms for the solution of real-world problems, in particular logistics problems. Elizabeth S. Burnside (“ Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors ”) is an associate professor and the vice chair of research in the Department of Radiology at the University of Wisconsin School of Medicine and Public Health. She received her M.D. degree combined with a master's in public health followed by a master's degree in medical informatics from Stanford University in the midst of her medical training. As a result, her research investigates the use of artificial intelligence methods to improve decision-making in the domain of breast imaging. She was elected a Fellow in the Society of Breast Imaging in 2004. Abel Cadenillas (“ Optimal Control of a Mean-Reverting Inventory ”) is professor in the Department of Finance and Management Science and the Department of Mathematical and Statistical Sciences of the University of Alberta. He received his Ph.D. in statistics from Columbia University. He recently became World Class University Distinguished Professor of Financial Engineering at Ajou University (awarded by the Korean Ministry of Education, Science and Technology). He is an associate editor of Mathematics and Financial Economics. His research interests include operations research, management sciences, finance, economics, and mathematics. His work has been published in the Journal of Financial Economics, the Journal of Economic Theory, Mathematical Finance, SIAM Journal on Control and Optimization, the Journal of Finance and Stochastics, and other journals. Bala G. Chandran (“ Algorithms for Scheduling Runway Operations Under Constrained Position Shifting ”) is a consultant at Analytics Operations Engineering, Inc. in Boston, Massachusetts. He received his Ph.D. from the Department of Industrial Engineering and Operations Research at the University of California, Berkeley, in April 2007. His research interests lie in combinatorial optimization and in algorithm development and implementation. Jagpreet Chhatwal (“ Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors ”) is a health economist at Merck Research Laboratories. His research interests include sequential decision making under uncertainty, medical decision making, and health economics. He received his Ph.D. in industrial engineering from the University of Wisconsin–Madison in 2008. His dissertation was awarded second place in the George B. Dantzig Dissertation Award competition in 2009. This work also earned for him the best student paper awards from the Decision Analysis Society, and the Manufacturing and Service Operations Management Society. Gregory Dobson (“ A Model of ICU Bumping ”) is an associate professor of operations management at the Simon School of Business, University of Rochester. He holds a B.S. in operations research and industrial engineering from Cornell University and a Ph.D. in operations research from Stanford University. His current work concentrates on the application of process improvement principles, in particular Six Sigma, to health care and other industries. Özlem Ergun (“ Network Design and Allocation Mechanisms for Carrier Alliances in Liner Shipping ”) is an associate professor in the School of Industrial and Systems Engineering at the Georgia Institute of Technology. She is also a cofounder and codirector of the Humanitarian Logistics Research Center at the Supply Chain and Logistics Institute at Georgia Tech. Her research focuses on the design and management of large-scale networks. Specifically, she studies logistics and communications networks that are dynamic and partially decentralized. She has recently focused on understanding how collaboration among different entities can help them to be more efficient and create value for the overall system. She has applied her work on network design, management, and collaboration to problems arising in the airline, ocean cargo, and trucking industries. Recently she has taken a leadership role in promoting the use of systems thinking and mathematical modeling in applications with societal impact within the INFORMS community. As codirector of the Center for Humanitarian Logistics at Georgia Tech, she has worked with organizations that respond to humanitarian crises around the world, including the United Nations World Food Programme, CARE USA, FEMA, USACE, CDC, AFCEMA, and MedShare International. Finn R. Førsund (“ Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis ”) is a professor at the University of Oslo, Department of Economics, where he received his doctor philosophie degree in 1983 based on production theory and efficiency analyses. His main research interests are within the fields of environmental economics, energy economics, production theory, and productivity and efficiency. He has published extensively in international journals. He is a scientific advisor to the Ragnar Frisch Centre for Economic Research in Oslo. Robert Fourer (“ Optimization Services: A Framework for Distributed Optimization ”) is professor of industrial engineering and management sciences at Northwestern University. He has a long-standing research interest in computer systems for the support of optimization and was one of the creators of the AMPL modeling language. Banu Gemici-Ozkan (“ R&D Project Portfolio Analysis for the Semiconductor Industry ”) is a revenue management science analyst in the Department of Revenue Management at Carnival Cruise Lines. She received her M.S. degree in management science (2004) and Ph.D. degree in operations research (2009), both from Lehigh University. Her main research interests are applying adaptive forecasting techniques as a part of large decision systems and optimization models. Paul Glasserman (“ Sensitivity Estimates from Characteristic Functions ”) is the Jack R. Anderson Professor of Business at Columbia Business School. He has held visiting positions at Princeton University, New York University, and the Federal Reserve Bank of New York. His research interests include stochastic modeling, simulation, derivative securities, and risk management. Nicholas G. Hall (“ Capacity Allocation and Scheduling in Supply Chains ”) is a professor of operations management at the Fisher College of Business, the Ohio State University. He received B.A. and M.A. degrees from the University of Cambridge and a Ph.D. degree from the University of California, Berkeley. His research interests include

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.364
Teacher spread0.326 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

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