000 04051nam a22005175i 4500
001 978-0-387-71909-2
003 DE-He213
005 20250710084014.0
007 cr nn 008mamaa
008 100301s2009 xxu| s |||| 0|eng d
020 _a9780387719092
_a99780387719092
024 7 _a10.1007/978-0-387-71909-2
_2doi
082 0 4 _a658.40301
_223
100 1 _aShi, Leyuan.
_eauthor.
245 1 0 _aNested Partitions Method, Theory and Applications
_h[recurso electrónico] /
_cby Leyuan Shi, Sigurdur Ólafsson.
264 1 _aBoston, MA :
_bSpringer US,
_c2009.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v109
505 0 _aMethodology -- The Nested Partitions Method -- Noisy Objective Functions -- Mathematical Programming in the NP Framework -- Hybrid Nested Partitions Algorithm -- Applications -- Flexible Resource Scheduling -- Feature Selection -- Supply Chain Network Design -- Beam Angle Selection -- Local Pickup and Delivery Problem -- Extended Job Shop Scheduling -- Resource Allocation under Uncertainty.
520 _aThere is increasing need to solve large-scale complex optimization problems in a wide variety of science and engineering applications, including designing telecommunication networks for multimedia transmission, planning and scheduling problems in manufacturing and military operations, or designing nanoscale devices and systems. Advances in technology and information systems have made such optimization problems more and more complicated in terms of size and uncertainty. Nested Partitions Method, Theory and Applications provides a cutting-edge research tool to use for large-scale, complex systems optimization. The Nested Partitions (NP) framework is an innovative mix of traditional optimization methodology and probabilistic assumptions. An important feature of the NP framework is that it combines many well-known optimization techniques, including dynamic programming, mixed integer programming, genetic algorithms and tabu search, while also integrating many problem-specific local search heuristics. The book uses numerous real-world application examples, demonstrating that the resulting hybrid algorithms are much more robust and efficient than a single stand-alone heuristic or optimization technique. This book aims to provide an optimization framework with which researchers will be able to discover and develop new hybrid optimization methods for successful application of real optimization problems. Researchers and practitioners in management science, industrial engineering, economics, computer science, and environmental science will find this book valuable in their research and study. Because of its emphasis on practical applications, the book can appropriately be used as a textbook in a graduate course.
650 0 _aECONOMICS.
650 0 _aCOMPUTER SCIENCE
_xMATHEMATICS.
650 0 _aMATHEMATICAL OPTIMIZATION.
650 0 _aOPERATIONS RESEARCH.
650 0 _aBUSINESS LOGISTICS.
650 1 4 _aECONOMICS/MANAGEMENT SCIENCE.
650 2 4 _aOPERATIONS RESEARCH/DECISION THEORY.
650 2 4 _aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650 2 4 _aOPTIMIZATION.
650 2 4 _aPRODUCTION/LOGISTICS.
650 2 4 _aCOMPUTATIONAL MATHEMATICS AND NUMERICAL ANALYSIS.
650 2 4 _aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS.
700 1 _aÓlafsson, Sigurdur.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387719085
830 0 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v109
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-71909-2
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-SBE
942 _2ddc
_cER
999 _c58308
_d58308