000			03585nam a22004815i 4500
001			978-3-319-03398-3
003			DE-He213
005			20200420221249.0
007			cr nn 008mamaa
008			140207s2014 gw | s |||| 0|eng d
020			_a9783319033983 _9978-3-319-03398-3
024	7		_a10.1007/978-3-319-03398-3 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aXu, Jiuping. _eauthor.
245	1	0	_aFuzzy-Like Multiple Objective Multistage Decision Making _h[electronic resource] / _cby Jiuping Xu, Ziqiang Zeng.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXXIV, 378 p. 111 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v533
505	0		_aMultiple Objective Multistage Decision Making -- Elements of Fuzzy-Like MOMSDM -- Fuzzy MOMSDM for Dynamic Machine Allocation -- Fuzzy MOMSDM for Closed Multiclass Queuing Networks -- Fuzzy Random MOMSDM for Inventory Management -- Fuzzy Random MOMSDM for Facilities Planning -- Fuzzy Random MOMSDM for Transportation Assignment.
520			_aDecision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like uncertainty, including the dynamic machine allocation, closed multiclass queueing networks optimization, inventory management, facilities planning and transportation assignment. A number of real-world engineering case studies are used to illustrate in detail the methodology. With its emphasis on problem-solving and applications, this book is ideal for researchers, practitioners, engineers, graduate students and upper-level undergraduates in applied mathematics, management science, operations research, information system, civil engineering, building construction and transportation optimization.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aZeng, Ziqiang. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319033976
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v533
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-03398-3
912			_aZDB-2-ENG
942			_cEBK
999			_c52482 _d52482