Optimization in Engineering

Models and Algorithms

  • Ramteen Sioshansi
  • Antonio J. Conejo
Part of the Springer Optimization and Its Applications book series (SOIA, volume 120)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ramteen Sioshansi, Antonio J. Conejo
    Pages 1-16
  3. Ramteen Sioshansi, Antonio J. Conejo
    Pages 17-121
  4. Ramteen Sioshansi, Antonio J. Conejo
    Pages 123-196
  5. Ramteen Sioshansi, Antonio J. Conejo
    Pages 197-285
  6. Ramteen Sioshansi, Antonio J. Conejo
    Pages 287-336
  7. Ramteen Sioshansi, Antonio J. Conejo
    Pages 337-388
  8. Back Matter
    Pages 389-412

About this book

Introduction

This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems.

The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields.

Keywords

linear programming modeling GAMS optimization energy optimization engineering optimization undergraduate engineering optimization undergraduate textbook graduate textbook industrial engineering dynamic optimization problem linear optimization problem mixed-integer optimization nonlinear optimization problem duality theory Taylor approximation

Authors and affiliations

  • Ramteen Sioshansi
    • 1
  • Antonio J. Conejo
    • 2
  1. 1.Department of Integrated Systems EngineeringThe Ohio State UniversityColumbusUSA
  2. 2.Department of Integrated Systems Engineering and Department of Electrical and Computer EngineeringThe Ohio State UniversityColumbusUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-56769-3
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-56767-9
  • Online ISBN 978-3-319-56769-3
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • About this book