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001			290108
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007			cr nn 008mamaa
008			150903s2013 xxu| o |||| 0|eng d
020			_a9781461480020 _99781461480020
024	7		_a10.1007/9781461480020 _2doi
035			_avtls000342360
039		9	_a201509030852 _bVLOAD _c201405050242 _dVLOAD _y201402061124 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA71-90
100	1		_aGoldengorin, Boris. _eautor _9317757
245	1	0	_aCell Formation in Industrial Engineering : _bTheory, Algorithms and Experiments / _cby Boris Goldengorin, Dmitry Krushinsky, Panos M. Pardalos.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_axiv, 206 páginas 52 ilustraciones, 39 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Optimization and Its Applications, _x1931-6828 ; _v79
500			_aSpringer eBooks
505	0		_a1. The problem of cell formation -- 2. The p-Median problem -- 3. Application of the PMP to cell formation in group technology -- 4. The minimum multicut problem and an exact model for cell formation -- 5. Multiobjective nature of cell formation -- 6. Pattern-based heuristic for the cell formation problem in group technology -- 7. Branch-and-bound algorithm for bi-criterion cell formation problems -- 8. Summary and conclusions -- A. Solutions to the 35 CF instances from [71] -- Index -- References.
520			_aThis book focuses on a development of optimal, flexible, and efficient models and algorithms for cell formation in group technology. Its main aim is to provide a reliable tool that can be used by managers and engineers to design manufacturing cells based on their own preferences and constraints imposed by a particular manufacturing system. This tool could potentially lower production costs by minimizing other costs in a number of areas, thereby increasing profit in a manufacturing system. In the volume, the cell formation problem is considered in a systematic and formalized way, and several models are proposed, both heuristic and exact. The models are based on general clustering problems, and are flexible enough to allow for various objectives and constraints. The authors also provide results of numerical experiments involving both artificial data from academic papers in the field and real manufacturing data to certify the appropriateness of the models proposed. The book was intended to suit the broadest possible audience, and thus all algorithmic details are given in a detailed description with multiple numerical examples and informal explanations are provided for the theoretical results. In addition to managers and industrial engineers, this book is intended for academic researchers and students. It will also be attractive to many theoreticians, since it addresses many open problems in computer science and bioinformatics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKrushinsky, Dmitry. _eautor _9320665
700	1		_aPardalos, Panos M. _eautor _9299588
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781461480013
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-8002-0 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c290108 _d290108