Operational Data Analytics

Foundations for Decision-Focused Regularized Learning
Erscheinungsjahr 2027
ISBN: 978-3-032-46878-9
Verlag: Springer

Buch, Englisch, Format (B × H): 168 mm x 240 mm

This book develops Operational Data Analytics (ODA) as a rigorous theoretical foundation, a unified research methodology, and a guiding philosophy for data-integrated decision making. Moving beyond conventional approaches that emphasize general-purpose learning and optimization procedures, ODA places the structure of the decision problem at the heart of decision learning. Its central focus is the design and validation of data-to-decision mappings, termed , that systematically exploit the knowledge and structural properties of the underlying system.  Bringing together optimization, statistical inference, machine learning, and universal approximation, the authors develop a unified spectrum of decision-learning approaches distinguished by what is known, what remains to be learned, and how decision quality should be validated. The book equips researchers and doctoral students with a rigorous framework for integrating advanced analytical methods with mathematical structure and operational insight.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Feng, Qi

Shanthikumar, J. George

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction.- Popular Existing Solution Approaches.- The Parametric ODA Framework.- The Optimality of Parametric ODA.- Beyond the Location-Scale Model.- ODA with Censored Observation.- Contexture-Based Models.- Multi-Dimensional Extension.- The Stopping Time Problem.- Dynamic Decisions with Censored Observations.- ODA Boosting.- Cross Learning and Co-Learning.- Appendix: Statistical Theories.

Über Autor(innen)

Qi (Annabelle) Feng, Ph.D., is the John and Donna Krenicki Chair in Operations Management at Purdue University’s Daniels School of Business. Her research focuses on stochastic functions, data-driven decision-making, and negotiations with applications spanning stochastic inventory and pricing models, supply chain structure, resource planning, not-for-profit operations, and proliferation management. A Fellow of the Production and Operations Management Society (POMS), Dr. Feng is a recipient of the Franz Edelman Award and the Wickham Skinner Early-Career Award. She holds a Ph.D. from University of Texas at Dallas.

J. George Shanthikumar, Ph.D., is the Richard E. Dauch Distinguished Chair in Manufacturing and Operations Management at Purdue University. He previously served as a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. His research integrates interdisciplinary decision-making, model uncertainty and learning, production systems, queueing theory, reliability, and scheduling. Additionally, his work encompasses semiconductor yield management, simulation, and stochastic processes. He is a Fellow of both the Institute for Operations Research and the Management Sciences (INFORMS) and the Production and Operations Management Society (POMS). He holds a Ph.D. from University of Toronto.