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＜電子ブック＞
Nested Partitions Method, Theory and Applications / by Leyuan Shi, Sigurdur Ólafsson
(International Series in Operations Research & Management Science. ISSN:22147934 ; 109)

版	1st ed. 2009.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	2009
本文言語	英語
大きさ	X, 260 p : online resource
著者標目	*Shi, Leyuan author Ólafsson, Sigurdur author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Operations research LCSH:Management science LCSH:Production management LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Mathematical models FREE:Optimization FREE:Operations Research and Decision Theory FREE:Operations Research, Management Science FREE:Operations Management FREE:Computational Mathematics and Numerical Analysis FREE:Mathematical Modeling and Industrial Mathematics
一般注記	Methodology -- 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 There 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. HTTP:URL=https://doi.org/10.1007/978-0-387-71909-2

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