Optimization in Engineering

1. Front Matter

   Pages i-xv

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2. Optimization is Ubiquitous

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 1-16

3. Linear Optimization

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 17-121

4. Mixed-Integer Linear Optimization

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 123-196

5. Nonlinear Optimization

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 197-285

6. Iterative Solution Algorithms for Nonlinear Optimization

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 287-336

7. Dynamic Optimization

   • Ramteen Sioshansi, Antonio J. Conejo

   Pages 337-388

8. Back Matter

   Pages 389-412

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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.

• 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

• Department of Integrated Systems Engineering, The Ohio State University, Columbus, USA

  Ramteen Sioshansi

• Department of Integrated Systems Engineering and Department of Electrical and Computer Engineering, The Ohio State University, Columbus, USA

  Antonio J. Conejo

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.

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