Название: Computational Stochastic Programming: Models, Algorithms, and ImplementationАвтор: Lewis Ntaimo
Издательство: Springer
Год: 2024
Страниц: 518
Язык: английский
Формат: pdf (true), epub
Размер: 40.5 MB
This 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.
This book is about stochastic programming (SP), a field of optimization that deals with mathematical programming problems involving uncertainty. SP problems are generally difficult to solve and the field has continued to evolve with contributions from various disciplines such as operations research, mathematics, and probability and statistics. SP has a wide range of application areas that include manufacturing, transportation, telecommunications, electricity power generation, health care, agriculture, forestry and wildfire management, mineral, oil and gas exploration, and finance. 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. Therefore, this book is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and some computer programming background. Specifically, computer programming knowledge is needed for model and algorithm implementation (coding) using available optimization software.
The most important features of this book include a focus on both risk-neutral and risk-averse models, a variety of real-life example applications of SP, decomposition algorithms, detailed illustrative numerical examples of the models and algorithms, and an emphasis on computational experimentation. This book takes a pragmatic approach and places emphasis on both theory and implementation of the models and algorithms for solving practical optimization problems. The benefits readers are expected to derive from this book include learning the following: (a) modeling real-life problems using SP; (b) translating theory into practical algorithms; (c) implementing models and algorithms on a computer; and (d) performing computational experiments in SP. The book is based on the author’s hands-on experience with computational implementation of the various models and algorithms for different real-life applications.
In essence, this book is appropriate for students and practitioners who are new to this exciting field, and as a reference for the seasoned SP experts. What makes this text different from existing books on the subject is the focus on illustrative numerical examples and an emphasis on computer implementation of the models and algorithms. This focus was borne out of incessant requests from students over the years for numerical examples to help them better understand SP models and algorithms. Readers conversant with SP can simply skip the numerical example illustrations without loss of continuity in the discourse.
Contents:
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