By Singiresu S. Rao
Helps you progress from conception to optimizing engineering platforms in nearly any industry
Now in its Fourth Edition, Professor Singiresu Rao's acclaimed textual content Engineering Optimization permits readers to speedy grasp and practice all of the vital optimization tools in use this present day throughout a wide variety of industries. protecting either the most recent and classical optimization equipment, the textual content begins with the fundamentals after which gradually builds to complicated rules and applications.
This entire textual content covers nonlinear, linear, geometric, dynamic, and stochastic programming options in addition to extra really expert equipment akin to multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. each one technique is gifted in transparent, basic language, making even the extra refined suggestions effortless to understand. furthermore, the writer provides:
Case examples that convey how each one procedure is utilized to resolve real-world difficulties throughout numerous industries
Review questions and difficulties on the finish of every bankruptcy to have interaction readers in utilizing their newfound talents and knowledge
Examples that exhibit using MATLAB® for the answer of alternative kinds of sensible optimization problems
References and bibliography on the finish of every bankruptcy for exploring issues in higher depth
Answers to study Questions to be had at the author's site to assist readers to check their knowing of the elemental concepts
With its emphasis on problem-solving and functions, Engineering Optimization is perfect for upper-level undergraduates and graduate scholars in mechanical, civil, electric, chemical, and aerospace engineering. moreover, the textual content is helping training engineers in nearly any layout greater, extra effective structures at much less cost.
Read Online or Download Engineering Optimization: Theory and Practice PDF
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Additional resources for Engineering Optimization: Theory and Practice
SOLUTION The breadth b in meters, the depth d in meters, and the area of reinforcing steel As in square meters are taken as the design variables x1 , x2 , and x3 , respectively (Fig. 13). 13 Cross section of a reinforced concrete beam. 95 (E2 ) where P [· · ·] indicates the probability of occurrence of the event [· · ·]. 542) (E3 ) fc 600 bd fs 600 + fs Since the design variables cannot be negative, we have d≥0 b≥0 As ≥ 0 (E4 ) Since the quantities M, fc , and fs are nondeterministic, the problem is a stochastic programming problem.
10) n aij xi = bj , j = 1, 2, . . , m i=1 xi ≥ 0, i = 1, 2, . . , n where ci , aij , and bj are constants. 6 A scaffolding system consists of three beams and six ropes as shown in Fig. 12. Each of the top ropes A and B can carry a load of W1 , each of the middle ropes C and D can carry a load of W2 , and each of the bottom ropes E and F can carry a load of W3 . If the loads acting on beams 1, 2, and 3 are x1 , x2 , and x3 , respectively, as shown in Fig. 12 Scaffolding system with three beams.
Nemhauser, Introduction to Dynamic Programming, Wiley, New York, 1966. L. Cooper and M. W. Cooper, Introduction to Dynamic Programming, Pergamon Press, Oxford, UK, 1981. W. B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality, Wiley, Hoboken, NJ, 2007. M. L. Puterman, Dynamic Programming and Its Applications, Academic Press, New York, 1978. M. Sniedovich, Dynamic Programming, Marcel Dekker, New York, 1992. 89 J. K. Sengupta, Stochastic Programming: Methods and Applications, North-Holland, Amsterdam, 1972.