Organizations and businesses strive toward excellence, and solutions to problems are based mostly on judgment and experience. However, increased competition and consumer demands require that the solutions be optimum and not just feasible. Theory leads to algorithms. Algorithms need to be translated into computer codes. Engineering problems need to be modeled. Optimum solutions are obtained using theory and computers, and then interpreted. Revised and expanded in its third edition, this textbook integrates theory, modeling, development of numerical methods, and problem solving, thus preparing students to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multi-objective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. It is ideal for advanced undergraduate or graduate courses in optimization design and for practicing engineers. 'A meaningful entry into the world of optimization, with useful figures and examples as well as MATLAB samples. Students will continue to discover a wealth of knowledge and gain insights on optimization theory and engineering design applications from this edition.' Soobum Lee, University of Maryland 'Optimization is one of the most fundamental concepts in engineering, and this new edition of the classic text by Belegundu and Chandrupatla provides a solid introduction to the underlying mathematics and algorithms. Students and practitioners will enjoy their clear explanations of the basic concepts and the many examples spanning the full spectrum of engineering disciplines.' Mykel Kochenderfer, Stanford University
Optimization Concepts and Applications in Engineering
Preface; 1. Preliminary concepts; 2. One-dimensional unconstrained minimization; 3. Unconstrained optimization; 4. Linear programming; 5. Constrained minimization; 6. Penalty functions, duality, and geometric programming; 7. Direct search methods for nonlinear optimization; 8. Multi-objective optimization; 9. Integer and discrete programming; 10. Dynamic programming; 11. Optimization applications for transportation, assignment, and network problems; 12. Finite element and simulation-based optimization; Index.