Principles of Inventory Management: When You Are Down to Four, Order More (Springer Series in Operations Research and Financial Engineering) - Tapa dura

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<p>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.&nbsp; This textbook examines basic mathematical techniques used&nbsp; to sufficiently manage inventories by using various computational methods and mathematical models.&nbsp; 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.<br><br><em>Principles of Inventory Management</em> begins with an introductory chapter in which the basics of inventory systems and mathematical assumptions for all models are grouped together.&nbsp; 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.&nbsp; It contains both deterministic and stochastic models along with algorithms that can be employed to find solutions to a variety of inventory control problems.<br><br>Key topics include:</p><ul><p></p></ul><p>* Economic order quantity (EOQ) model</p><ul><p></p><p></p></ul><p>* Power-of-two policies</p><ul><p></p><p></p></ul><p>* Dynamic lot sizing</p><ul><p></p><p></p></ul><p>* Single and multi-period stochastic models&nbsp;&nbsp;&nbsp; </p><ul><p></p><p></p></ul><p>* Echelon-based approaches</p><ul><p></p><p></p></ul><p>* Multi-echelon systems</p><ul><p></p><p></p></ul><p>* Single and multi-item models</p><p><br>With exercises at the end of each chapter and a clear, systematic exposition,&nbsp; 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.&nbsp; The prerequisite courses include introductory optimization methods, probability theory (non-measure theoretic), and stochastic processes.</p>