Nested Partitions Method, Theory and Applications [electronic resource] / by Leyuan Shi, Sigurdur Ólafsson.
Contributor(s): Ólafsson, Sigurdur [author.] | SpringerLink (Online service)Material type: TextSeries: International Series in Operations Research & Management Science: 109Publisher: Boston, MA : Springer US, 2009Description: X, 260 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387719092Subject(s): Mathematics | Production management | Operations research | Decision making | Computer mathematics | Mathematical models | Mathematical optimization | Management science | Mathematics | Optimization | Operation Research/Decision Theory | Operations Research, Management Science | Operations Management | Computational Mathematics and Numerical Analysis | Mathematical Modeling and Industrial MathematicsAdditional physical formats: Printed edition:: No titleDDC classification: 519.6 LOC classification: QA402.5-402.6Online resources: Click here to access online
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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. .