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Enregistrement W4238914673 · doi:10.1287/opre.1110.0981

Contributors

2011· article· en· W4238914673 sur OpenAlexaboutno aff

Notice bibliographique

RevueOperations Research · 2011
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueSupply Chain and Inventory Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

Hyun-Soo Ahn (“ Dynamic Pricing of Limited Inventories When Customers Negotiate ”) is an associate professor of operations and management science at the Stephen M. Ross School of Business at the University of Michigan. His research interests include analysis of production and service operation systems, supply chain management, and revenue management in retailing and service systems. Daniel W. Apley (“ Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation ”) is an associate professor of industrial engineering and management sciences at Northwestern University. His research interests lie at the interface of engineering and systems modeling, statistical analysis, and data mining. For his work in this area, he received the NSF CAREER award in 2001, the IIE Transactions Best Paper Award in 2003, and the Wilcoxon Prize for best practical application paper appearing in Technometrics in 2008. He currently serves as editor-in-chief for the Journal of Quality Technology and has served as chair of the Quality, Statistics, and Reliability Section of INFORMS, director of the Manufacturing and Design Engineering Program at Northwestern University, and associate editor for Technometrics. Göker Aydin (“ Dynamic Pricing of Limited Inventories When Customers Negotiate ”) is an associate professor of operations and decision technologies at the Kelley School of Business at Indiana University, Bloomington. His research interests include retail operations and supply chain management, with an emphasis on revenue management in retailing and its supply chain implications. Güzin Bayraksan (“ A Sequential Sampling Procedure for Stochastic Programming ”) is an assistant professor of systems and industrial engineering at the University of Arizona. She holds a B.S. degree in industrial engineering from Boğaziçi University and M.S. and Ph.D. degrees in operations research and industrial engineering from the University of Texas at Austin. Her research focuses on stochastic optimization, particularly simulation-based approximations in stochastic programming with applications in scheduling and water resources management. Massimiliano Caramia (“ An Economic Model for Resource Allocation in Grid Computing ”) is associate professor of operations research at the University of Rome “Tor Vergata.” He received his Ph.D. degree in operations research in 2000 from the University of Rome “La Sapienza” and was a researcher at the Istituto per le Applicazioni del Calcolo of the Italian National Research Council, 2001–2006. His main research interests are scheduling, graph theory, optimization, logistics, transportation, and production systems. He has published in journals such as Networks, Operations Research, Discrete Mathematics, Discrete Applied Mathematics, Journal of Heuristics, INFORMS Journal on Computing, SIAM Journal on Optimization, Computers & Operations Research, and Operations Research Letters. Li Chen (“ Tight Bounds for Some Risk Measures, with Applications to Robust Portfolio Selection ”) is a quantitative analyst in Société Générale CIB HK. She received her Ph.D. in operations research/optimization from the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong and a bachelor's degree in mathematics from the University of Science and Technology of China. Her research interests are related to risk management and optimization. Arun Chockalingam (“ American Options Under Stochastic Volatility ”) is a visiting assistant professor in the School of Industrial Engineering at Purdue University. He obtained his doctoral degree in industrial engineering from Purdue University in 2008. His research interests lie in solving decision-making problems that arise in financial engineering and health-care systems utilizing stochastic control techniques. Wade D. Cook (“ Multiple Variable Proportionality in Data Envelopment Analysis ”) is the Gordon Charlton Shaw Professor of Management Science in the Schulich School of Business, York University, Toronto, Ontario, Canada, where he serves as department head of management science and as associate dean of research. He holds a doctorate in mathematics and operations research. He has published several books and more than 140 articles in a wide range of academic and professional journals, including Management Science, Operations Research, Journal of the Operational Research Society, European Journal of Operational Research, and IIE Transactions. His areas of specialty include data envelopment analysis and multicriteria decision modeling. He is a former editor of the Journal of Productivity Analysis and INFOR, and he is currently an associate editor of Operations Research. He has consulted widely with various companies and government agencies. Izak Duenyas (“ Purchasing Under Asymmetric Demand and Cost Information: When Is More Private Information Better? ”) is the John Psarouthakis Professor of Manufacturing Management and professor and chair of operations and management science at the Ross School of Business at the University of Michigan. His research interests are in supply chain coordination, capacity investments, and optimal control of production systems. Awi Federguen (“ Procurement Strategies with Unreliable Suppliers ”) is the Charles E. Exley Professor of Management at the Graduate School of Business at Columbia University. He is an expert in the development and implementation of planning models for supply chain management and logistical systems. Stefano Giordani (“ An Economic Model for Resource Allocation in Grid Computing ”) is associate professor of operations research at the University of Rome “Tor Vergata.” He received his Ph.D. degree in operations research in 1999 from the University of Rome “La Sapienza.” His main research interests are scheduling, graph theory, optimization, logistics, transportation, and production systems. He has published in journals such as Networks, Discrete Mathematics, Discrete Applied Mathematics, Journal of Heuristics, Annals of Operations Research, Computational Optimization and Applications, Information Processing Letters, and Computers & Operations Research. Peter W. Glynn (“ A Complementarity Framework for Forward Contracting Under Uncertainty ”) received his Ph.D. in operations research from Stanford University in 1982. He joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in computer science and mathematics. In 1987 he returned to Stanford, where he joined the Department of Operations Research. He is the Thomas Ford Professor of Engineering in the Department of Management Science and Engineering and holds a courtesy appointment in the Department of Electrical Engineering. From 1999 to 2005 he served as deputy chair of the Department of Management Science and Engineering, and he was director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics. He was a cowinner of Best Publication Awards from the INFORMS Simulation Society in 1993 and 2008, and was a cowinner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009. In 2010 he was awarded the John von Neumann Theory Prize by INFORMS. His research interests lie in computational probability, queueing theory, statistical inference for stochastic processes, and stochastic modeling. Joel Goh (“ Robust Optimization Made Easy with ROME ”) is a Ph.D. student in operations, information, and technology at the Stanford Graduate School of Business. He was formerly an instructor of decision sciences at the NUS Business School, National University of Singapore (NUS). Linda V. Green (“ Identifying Good Nursing Levels: A Queuing Approach ”) is the Armand G. Erpf Professor at Columbia Business School. Her research in recent years has focused on the development and application of stochastic models to identify operational policies to improve health-care delivery. Specific applications include emergency room (ER) physician staffing, physician panel sizing, hospital bed capacity planning, scheduling of imaging equipment, nurse staffing, stroke patient management, and burn disaster triage. Current projects include estimating primary physician capacity needs; studying the interrelationship of obstetrics bed capacity, delivery methods, and adverse clinical outcomes; and identifying policies for improving timely physician access in ERs. Pengfei Guo (“ Strategic Behavior and Social Optimization in Markovian Vacation Queues ”) is an assistant professor in the Department of Logistics and Maritime Studies at Hong Kong Polytechnic University. He received his Ph.D. from Duke University in 2007. His research mainly focuses on the design and control of queueing systems with customers' decentralized behavior considered. He is also conducting research on multitier supply chains. Refael Hassin (“ Strategic Behavior and Social Optimization in Markovian Vacation Queues ”) is a professor of operations research at Tel Aviv University. His main research interests are combinatorial optimization and queueing models that involve strategic decisions. Simai He (“ Tight Bounds for Some Risk Measures, with Applications to Robust Portfolio Selection ”) is an assistant professor in the Department of Management Sciences at the City University of Hong Kong. His research interests are related to optimization models, approximation algorithms, and game theory. Woonghee Tim

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,897
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0080,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,244
Tête enseignante GPT0,339
Écart entre enseignants0,095 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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