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OverviewThis book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book’s most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example applications of stochastic programming, decomposition algorithms, detailed illustrative numerical examples of the models and algorithms, and an emphasis on computational experimentation. With a focus on both theory and implementation of the models and algorithms for solving practical optimization problems, this monograph is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and some computer programming background. Several examples of stochastic programming applications areincluded, providing numerical examples to illustrate the models and algorithms for both stochastic linear and mixed-integer programming, and showing the reader how to implement the models and algorithms using computer software. Full Product DetailsAuthor: Lewis NtaimoPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2024 Volume: 774 ISBN: 9783031524622ISBN 10: 3031524624 Pages: 509 Publication Date: 05 April 2024 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of Contents1. Introduction.- 2 Stochastic Programming Models.- 3 Modeling and Illustrative Numerical Examples.- 4 Example Applications of Stochastic Programming.- 5 Deterministic Large-Scale Decomposition Methods.- 6 Risk-Neutral Stochastic Linear Programming Methods.- 7 Mean-Risk Stochastic Linear Programming Methods.- 8 Sampling-Based Stochastic Linear Programming Methods.- 9 Stochastic Mixed-Integer Programming Methods.- 10 Computational Experimentation.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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