000			03833nam a22004935i 4500
001			978-0-387-36951-8
003			DE-He213
005			20250710083957.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387369518 _a99780387369518
024	7		_a10.1007/978-0-387-36951-8 _2doi
082	0	4	_a519.6 _223
100	1		_aHu, Qiying. _eauthor.
245	1	0	_aMarkov Decision Processes With Their Applications _h[recurso electrónico] / _cby Qiying Hu, Wuyi Yue.
264		1	_aBoston, MA : _bSpringer US, _c2008.
300			_aXVI, 298 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Mechanics and Mathematics ; _v14
505	0		_aDiscretetimemarkovdecisionprocesses: Total Reward -- Discretetimemarkovdecisionprocesses: Average Criterion -- Continuous Time Markov Decision Processes -- Semi-Markov Decision Processes -- Markovdecisionprocessesinsemi-Markov Environments -- Optimal control of discrete event systems: I -- Optimal control of discrete event systems: II -- Optimal replacement under stochastic Environments -- Optimalal location in sequential online Auctions.
520			_aMarkov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems: *a new methodology for MDPs with discounted total reward criterion; *transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; *MDPs in stochastic environments, which greatly extends the area where MDPs can be applied; *applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.
650		0	_aMATHEMATICS.
650		0	_aMATHEMATICAL OPTIMIZATION.
650		0	_aOPERATIONS RESEARCH.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aINDUSTRIAL ENGINEERING.
650	1	4	_aMATHEMATICS.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aCALCULUS OF VARIATIONS AND OPTIMAL CONTROL; OPTIMIZATION.
650	2	4	_aINDUSTRIAL AND PRODUCTION ENGINEERING.
700	1		_aYue, Wuyi. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387369501
830		0	_aAdvances in Mechanics and Mathematics ; _v14
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-36951-8 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c57542 _d57542