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Principles of Inventory Management

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  • John A. Muckstadt
  • Amar Sapra

Table of contents

  1. Front Matter
    Pages 1-15
  2. John A. Muckstadt, Amar Sapra
    Pages 1-16
  3. John A. Muckstadt, Amar Sapra
    Pages 17-45
  4. John A. Muckstadt, Amar Sapra
    Pages 47-84
  5. John A. Muckstadt, Amar Sapra
    Pages 85-112
  6. John A. Muckstadt, Amar Sapra
    Pages 113-140
  7. John A. Muckstadt, Amar Sapra
    Pages 141-183
  8. John A. Muckstadt, Amar Sapra
    Pages 237-292
  9. John A. Muckstadt, Amar Sapra
    Pages 293-318
  10. Back Matter
    Pages 1-21

About this book

Introduction

Inventories are prevalent everywhere in the commercial world, whether it be in retail stores, manufacturing facilities, government stockpile material, Federal Reserve banks, or even your own household.  This textbook examines basic mathematical techniques used  to sufficiently manage inventories by using various computational methods and mathematical models.  Such models discussed include: EOQ model and extensions, power-of-two models, single and multi-period models, probabilistic lot sizing models, multi-echelon stochastic models, Laplace and Normal demand models, exact Poisson model, and many more.

Principles of Inventory Management begins with an introductory chapter in which the basics of inventory systems and mathematical assumptions for all models are grouped together.  The text is presented in a way such that each section can be read independently, and so the order in which the reader approaches the book can be inconsequential.  It contains both deterministic and stochastic models along with algorithms that can be employed to find solutions to a variety of inventory control problems.

Key topics include:

* Economic order quantity (EOQ) model

* Power-of-two policies

* Dynamic lot sizing

* Single and multi-period stochastic models   

* Echelon-based approaches

* Multi-echelon systems

* Single and multi-item models


With exercises at the end of each chapter and a clear, systematic exposition,  this textbook will appeal to advanced undergraduate and first-year graduate students in operations research, industrial engineering, and quantitative MBA programs. It also serves as a reference for professionals in both industry and government worlds.  The prerequisite courses include introductory optimization methods, probability theory (non-measure theoretic), and stochastic processes.

Keywords

Analysis Optimization Methods Stochastic Processes Stochastic model Stochastic models algorithms distribution operations research optimization

Authors and affiliations

  • John A. Muckstadt
    • 1
  • Amar Sapra
    • 2
  1. 1.School of Operations Research &Cornell UniversityIthacaU.S.A.
  2. 2.and Information Systems,Department of Quantatative MethodsBangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-68948-7
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-24492-1
  • Online ISBN 978-0-387-68948-7
  • Series Print ISSN 1431-8598
  • Buy this book on publisher's site