Description
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.
Table of Contents
1. Optimization is Ubiquitous.- 2. Linear Optimization.- 3. Mixed-Integer Linear Optimization.- 4. Nonlinear Optimization.- 5. Iterative Solution Algorithms for Nonlinear Optimization.- 6. Dynamic Optimization.- A. Taylor Approximations and Definite Matrices.- B. Convexity.- Index.
