000			04185nam a22004935i 4500
001			978-3-540-78484-5
003			DE-He213
005			20161121230547.0
007			cr nn 008mamaa
008			100301s2008 gw | s |||| 0|eng d
020			_a9783540784845 _9978-3-540-78484-5
024	7		_a10.1007/978-3-540-78484-5 _2doi
050		4	_aQA402.5-402.6
072		7	_aPBU _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a519.6 _223
100	1		_aKasperski, Adam. _eauthor.
245	1	0	_aDiscrete Optimization with Interval Data _h[electronic resource] : _bMinmax Regret and Fuzzy Approach / _cby Adam Kasperski.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008.
300			_aXVI, 220 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v228
505	0		_aMinmax Regret Combinatorial Optimization Problems with Interval Data -- Problem Formulation -- Evaluation of Optimality of Solutions and Elements -- Exact Algorithms -- Approximation Algorithms -- Minmax Regret Minimum Selecting Items -- Minmax Regret Minimum Spanning Tree -- Minmax Regret Shortest Path -- Minmax Regret Minimum Assignment -- Minmax Regret Minimum s???t Cut -- Fuzzy Combinatorial Optimization Problem -- Conclusions and Open Problems -- Minmax Regret Sequencing Problems with Interval Data -- Problem Formulation -- Sequencing Problem with Maximum Lateness Criterion -- Sequencing Problem with Weighted Number of Late Jobs -- Sequencing Problem with the Total Flow Time Criterion -- Conclusions and Open Problems -- Discrete Scenario Representation of Uncertainty.
520			_aIn operations research applications we are often faced with the problem of incomplete or uncertain data. This book considers solving combinatorial optimization problems with imprecise data modeled by intervals and fuzzy intervals. It focuses on some basic and traditional problems, such as minimum spanning tree, shortest path, minimum assignment, minimum cut and various sequencing problems. The interval based approach has become very popular in the recent decade. Decision makers are often interested in hedging against the risk of poor (worst case) system performance. This is particularly important for decisions that are encountered only once. In order to compute a solution that behaves reasonably under any likely input data, the maximal regret criterion is widely used. Under this criterion we seek a solution that minimizes the largest deviation from optimum over all possible realizations of the input data. The minmax regret approach to discrete optimization with interval data has attracted considerable attention in the recent decade. This book summarizes the state of the art in the area and addresses some open problems. Furthermore, it contains a chapter devoted to the extension of the framework to the case when fuzzy intervals are applied to model uncertain data. The fuzzy intervals allow a more sophisticated uncertainty evaluation in the setting of possibility theory. This book is a valuable source of information for all operations research practitioners who are interested in modern approaches to problem solving. Apart from the description of the theoretical framework, it also presents some algorithms that can be applied to solve problems that arise in practice.
650		0	_aMathematics.
650		0	_aArtificial intelligence.
650		0	_aMathematical optimization.
650		0	_aApplied mathematics.
650		0	_aEngineering mathematics.
650	1	4	_aMathematics.
650	2	4	_aOptimization.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aArtificial Intelligence (incl. Robotics).
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783540784838
830		0	_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v228
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-540-78484-5
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c500591 _d500591