Brian T Denton (35 results)

Condition: New.

paperback. Condition: Very Good.

Hardcover. Condition: Very Good. 2013. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condition: Very Good. Very Good+; Hardcover; Covers are still glossy; Unblemished textblock edges; The endpapers and all text pages are clean and unmarked; The binding is excellent with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); 2.0 lbs; Dark red and orange covers with title in white lettering; 2013, Springer-Verlag Publishing; 536 pages; "Handbook of Healthcare Operations Management: Methods and Applications (International Series in Operations Research & Management Science)," by Brian T. Denton. …

Condition: New. 2026. 1st Edition. hardcover. . . . . .

Hardcover. Condition: Near Fine. Near Fine; minor bumping/rubbing to extremities; else As New. No dust jacket. Book.

Hardback. Condition: New. Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problems Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented Practice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teaching A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.…

Condition: New. 2026. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Buch. Condition: Neu. Neuware - Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. Readers will also find: - Coverage of artificial intelligence techniques applied to sequential decision-making problems- Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations- Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented- Practice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teaching- A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.…

Condition: New.

Condition: New.

Condition: New.

Hardback. Condition: New. Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problems Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented Practice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teaching A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.…

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Taschenbuch. Condition: Neu. Handbook of Healthcare Operations Management | Methods and Applications | Brian T. Denton | Taschenbuch | International Series in Operations Research & Management Science | x | Englisch | 2015 | Springer | EAN 9781489995506 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Condition: As New. Unread book in perfect condition.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - From the Preface:Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States.Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 - 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.…

Hardcover. Condition: Brand New. 1st edition. 536 pages. 9.25x6.25x1.25 inches. In Stock.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condition: new. Hardcover. Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructors solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problems Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented Practice exercises with solutions and an instructors manual designed to support both self-study and classroom-based teaching A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructors solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problems Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented Practice exercises with solutions and an instructors manual designed to support both self-study and classroom-based teaching A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: Brand New. 240 pages. 8.50x0.56x11.00 inches. In Stock. This item is printed on demand.

Condition: new. Questo è un articolo print on demand.

Condition: new. Questo è un articolo print on demand.

Hardcover. Condition: new. Hardcover. Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructors solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problems Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented Practice exercises with solutions and an instructors manual designed to support both self-study and classroom-based teaching A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the state of the art in the full breadth of health care operations managementThe single most comprehensive source of information in the fieldEditor and contributors are among the most prominent names in healthcare OR and OM.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the state of the art in the full breadth of health care operations managementThe single most comprehensive source of information in the fieldEditor and contributors are among the most prominent names in healthcare OR and OM.…