Optimization techniques for problem solving in uncertainty için kapak resmi
Optimization techniques for problem solving in uncertainty
Yayın Bilgileri:
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :

IGI Global,

Fiziksel Tanımlama:
18 PDFs (xiv, 313 pages)
Chapter 1. A Survey on grey optimization -- Chapter 2. Grey optimization problems using prey-predator algorithm -- Chapter 3. Optimality principles for fuzzy dual uncertain systems -- Chapter 4. A study of fully fuzzy linear fractional programming problems by signed distance ranking technique -- Chapter 5. Advances in fuzzy dynamic programming -- Chapter 6. Selected topics in robust optimization -- Chapter 7. A fuzzy measure of vulnerability for the optimization of vehicle routing problems with time windows -- Chapter 8. Experimental investigation for performance optimization of biodiesel-fueled diesel engine using Taguchi-Gray relational analysis -- Chapter 9. Land cover classification using the proposed texture model and fuzzy k-NN classifier -- Chapter 10. Supply chain network design in uncertain environment: a review and classification of related models.
"This book is devoted to recent developments, reviews and tutorials of decision making or optimization under uncertainty. Different models ranging from stochastic to grey and their connection will be outlines with highlighting possible future works along with applications on real case problems"-- Provided by publisher.
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E-Kitap 2470270-1001 T57.95 .O68 2018e IGI InfoSci

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When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects. Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.