000 | 02971nam a22004815i 4500 | ||
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001 | 978-3-540-76290-4 | ||
003 | DE-He213 | ||
005 | 20170628034811.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2008 gw | s |||| 0|eng d | ||
020 |
_a9783540762904 _9978-3-540-76290-4 |
||
024 | 7 |
_a10.1007/978-3-540-76290-4 _2doi |
|
050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
|
072 | 7 |
_aMAT003000 _2bisacsh |
|
082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aBuckley, James J. _eauthor. |
|
245 | 1 | 0 |
_aMonte Carlo Methods in Fuzzy Optimization _h[electronic resource] / _cby James J. Buckley, Leonard J. Jowers. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008. |
|
300 |
_aXIII, 260 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v222 |
|
505 | 0 | _aFuzzy 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. | |
520 | _aThis book is a concise and readable introduction to Monte Carlo methods to find good approximate solutions to fuzzy optimization problems. Various basic applications and illustrative examples are presented in an understandable way. The aim of the book is to convince the reader that Monte Carlo methods can be useful in generating approximate solutions to fuzzy optimization problems. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aEngineering mathematics. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aJowers, Leonard J. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540762898 |
830 | 0 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v222 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-76290-4 |
912 | _aZDB-2-ENG | ||
999 |
_c21582 _d21582 |