Introduction
The objective of this book is to introduce Monte Carlo methods to find 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 on in the book, like ≤ and < between fuzzy numbers since there will probably be fuzzy constraints, and how do we evaluate \(max/min\overline{Z}\) for \(\overline{Z}\) 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) Part III, comprising Chapters 17-27, outlines our “unfinished business” which are fuzzy optimization problems for which we have not yet applied our Monte Carlo method to produce approximate solutions; and (4) Part IV is our summary, conclusions and future research.
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Buckley, J.J., Jowers, L.J. (2007). Introduction. In: Monte Carlo Methods in Fuzzy Optimization. Studies in Fuzziness and Soft Computing, vol 222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76290-4_1
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DOI: https://doi.org/10.1007/978-3-540-76290-4_1
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