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.

Offers a problem-solving approach and a large number of illustrative examples leading to a step-by-step formulation and solving of optimization problems Discussions are based on real-world examples and case studies Clarity of presentation maintains mathematical rigor Optimization textbook with broad appeal expressly for engineering upper-undergraduates/graduate students Includes supplementary material: sn.pub/extras

Autorentext
Ramteen Sioshansi is an associate professor in the Department of Integrated Systems Engineering at The Ohio State University. He holds a B.A., M.S., and Ph.D. from the University of California, Berkeley and an M.Sc. from the London School of Economics and Political Science. He has published over 50 peer-reviewed journals and has been the principal investigator of many research projects sponsored by public agencies and private industry. Antonio J. Conejo, professor at The Ohio State University, OH, US, received the B.S from Univ. P. Comillas, Spain, the M.S. from MIT, US and the Ph.D. from the Royal Institute of Technology, Sweden. He has published over 190 papers in SCI journals and is the author or coauthor of books published by Springer, John Wiley, McGraw-Hill and CRC. He has been the principal investigator of many research projects financed by public agencies and the power industry and has supervised 19 PhD theses. He is an IEEE Fellow.

Zusammenfassung
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.

Inhalt

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.