Monte Carlo Methods in Fuzzy Optimization
(Sprache: Englisch)
1. 1 Introduction The objective of this book is to introduce Monte Carlo methods to ?nd good approximate solutions to fuzzy optimization problems. Many crisp (nonfuzzy) optimization problems have algorithms to determine solutions. This is not true for fuzzy...
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Klappentext zu „Monte Carlo Methods in Fuzzy Optimization “
1. 1 Introduction The objective of this book is to introduce Monte Carlo methods to ?nd good approximate solutions to fuzzy optimization problems. Many crisp (nonfuzzy) optimization problems have algorithms to determine solutions. This is not true for fuzzy optimization. There are other things to discuss in fuzzy optimization, which we will do later onin the book, like? and < between fuzzy numbers since there will probably be fuzzy constraints, and how do we evaluate max/minZ for Z the fuzzy value of the objective function. This book is divided into four parts: (1) Part I is the Introduction containing Chapters 1-5; (2) Part II, Chapters 6-16, has the applications of our Monte Carlo method to obtain approximate solutions to fuzzy optimization problems; (3)PartIII,comprisingChapters17-27,outlinesour"un?nishedbusiness"which are fuzzy optimization problems for which we have not yet applied our Monte Carlomethodtoproduceapproximatesolutions;and(4)PartIVisoursummary, conclusions and future research. 1. 1. 1 Part I First we need to be familiar with fuzzy sets. All you need to know about fuzzy sets for this book comprises Chapter 2. For a beginning introduction to fuzzy sets and fuzzy logic see [2]. Three other items related to fuzzy sets, needed in this book, are also in Chapter 2: (1) in Section 2. 5 we discuss how we have dealt in the past with determining max/min(Z)for Z a fuzzy set representing the value of anobjective function in a fuzzy optimization problem; (2) in Section 2.
Inhaltsverzeichnis zu „Monte Carlo Methods in Fuzzy Optimization “
Fuzzy Sets.- Crisp Random Numbers and Vectors.- Random Fuzzy Numbers and Vectors.- Tests for Randomness.- Applications.- Fuzzy Monte Carlo Method.- Fully Fuzzified Linear Programming I.- Fully Fuzzified Linear Programming II.- Fuzzy Multiobjective LP.- Solving Fuzzy Equations.- Fuzzy Linear Regression I.- Univariate Fuzzy Nonlinear Regression.- Multivariate Nonlinear Regression.- Fuzzy Linear Regression II.- Fuzzy Two-Person Zero-Sum Games.- Fuzzy Queuing Models.- Unfinished Business.- Fuzzy Min-Cost Capacitated Network.- Fuzzy Shortest Path Problem.- Fuzzy Max-Flow Problem.- Inventory Control: Known Demand.- Inventory Control: Fuzzy Demand.- Inventory Control: Backordering.- Fuzzy Transportation Problem.- Fuzzy Integer Programming.- Fuzzy Dynamic Programming.- Fuzzy Project Scheduling/PERT.- Max/Min Fuzzy Function.- Summary, Conclusions, Future Research.- Summary, Conclusions, Future Research.
Bibliographische Angaben
- Autoren: James J. Buckley , Leonard J. Jowers
- 2010, XIII, 260 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 364209516X
- ISBN-13: 9783642095160
Sprache:
Englisch
Pressezitat
From the reviews: "This timely research monograph is a very much needed compendium of recent developments in the methodologies and applications of Monte Carlo fuzzy optimization and fuzzy modeling. ... Overall the writing is lucid and well supported by convincing and highly motivating comments. ... All in all, this is a highly welcome publication which will undoubtedly appeal to the fuzzy set research community." (Witold Pedrycz, Zentralblatt MATH, Vol. 1148, 2008)
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