Ordinal Optimization
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Ordinal Optimization
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Author : Yu-Chi Ho
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-23
Ordinal Optimization written by Yu-Chi Ho and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-23 with Technology & Engineering categories.
Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succinct mathematical models. The purpose of this book is to address the difficulties of the optimization of complex systems via simulation models or other computation-intensive models involving possible stochastic effects and discrete choices. This book establishes distinct advantages of the "softer" ordinal approach for search-based type problems, analyzes its general properties, and shows the many orders of magnitude improvement in computational efficiency that is possible.
Stochastic Simulation Optimization For Discrete Event Systems Perturbation Analysis Ordinal Optimization And Beyond
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Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2013-07-03
Stochastic Simulation Optimization For Discrete Event Systems Perturbation Analysis Ordinal Optimization And Beyond written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-03 with Technology & Engineering categories.
Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a “hard nut to crack”. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions.
Power System Optimization
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Author : Haoyong Chen
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-15
Power System Optimization written by Haoyong Chen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-15 with Technology & Engineering categories.
An original look from a microeconomic perspective for power system optimization and its application to electricity markets Presents a new and systematic viewpoint for power system optimization inspired by microeconomics and game theory A timely and important advanced reference with the fast growth of smart grids Professor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest research A companion website is available Edit
Handbook Of Simulation Optimization
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Author : Michael C Fu
language : en
Publisher: Springer
Release Date : 2014-11-13
Handbook Of Simulation Optimization written by Michael C Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-13 with Business & Economics categories.
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science, operations management and stochastic control, as well as in economics/finance and computer science.
Stochastic Simulation Optimization An Optimal Computing Budget Allocation
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Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2010-06-04
Stochastic Simulation Optimization An Optimal Computing Budget Allocation written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-04 with Computers categories.
With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive.Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.
Optimal Control And Optimization Of Stochastic Supply Chain Systems
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Author : Dong-Ping Song
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-29
Optimal Control And Optimization Of Stochastic Supply Chain Systems written by Dong-Ping Song and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-29 with Technology & Engineering categories.
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of the complex analytical models produced by the requirements of operational practice, and the simple solutions needed for implementation. The analytical models and theoretical analysis propounded in this monograph will be of benefit to academic researchers and graduate students looking at logistics and supply chain management from standpoints in operations research or industrial, manufacturing, or control engineering. The practical tools and solutions and the qualitative insights into the ideas underlying functional supply chain systems will be of similar use to readers from more industrially-based backgrounds.
Discrete Manufacturing Process Design Optimization
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Author : Richard E. Nance
language : en
Publisher:
Release Date : 2001
Discrete Manufacturing Process Design Optimization written by Richard E. Nance and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computer science categories.
Discrete manufacturing process design optimization (DMPDO) is a problem of significant importance and interest to the Air Force. Moreover, the complexity of parts that must be manufactured for airplane engines and other units makes this problem extremely difficult. This report summarizes the research that has been conducted in developing the generalized hill climbing (GHC) algorithm framework for discrete optimization problems in general, and the DMPDO problems in particular. Convergence conditions for GHC algorithms have been developed. Ordinal hill climbing (OHC) algorithms were introduced to exploit the efficiency of ordinal optimization and the effectiveness of GHC algorithms to obtain a new class of discrete optimization problem algorithms. The relationship between OHC algorithms and genetic algorithms was also studied. Multiple sequence optimization using GHC algorithms were introduced to optimize across sets of DMPDO sequences. Simultaneous GHC algorithms were introduced as a generalization to optimize across a set of related discrete optimization problems. On-going interactions with researchers at the Materials Process Design Branch of the AFRL at WPAFB and at Austral Engineering and Software, Inc. has resulted in these algorithms being transitioned for application into commercial software tools designed to solve various DMPDO problems of interest to the Air Force.
Algorithmic Decision Theory
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Author : RONEN BRAFMAN
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-07
Algorithmic Decision Theory written by RONEN BRAFMAN and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-07 with Business & Economics categories.
This book constitutes the refereed proceedings of the Second International Conference on Algorithmic Decision Theory, ADT 2011, held in Piscataway, NJ, USA, in October 2011. The 24 revised full papers presented were carefully reviewed and selected from 50 submissions.
Communications And Networking
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Author : Bo Li
language : en
Publisher: Springer
Release Date : 2018-03-26
Communications And Networking written by Bo Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-26 with Computers categories.
The two-volume set LNICST 236-237 constitutes the post-conference proceedings of the 12th EAI International Conference on Communications and Networking, ChinaCom 2017, held in Xi’an, China, in September 2017. The total of 112 contributions presented in these volumes are carefully reviewed and selected from 178 submissions. The papers are organized in topical sections on wireless communications and networking, satellite and space communications and networking, big data network track, multimedia communications and smart networking, signal processing and communications, network and information security, advances and trends of V2X networks.
Learning Automata And Their Applications To Intelligent Systems
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Author : JunQi Zhang
language : en
Publisher: John Wiley & Sons
Release Date : 2023-11-10
Learning Automata And Their Applications To Intelligent Systems written by JunQi Zhang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Technology & Engineering categories.
Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested. Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as: Mathematical analysis of the behavior of learning automata, along with suitable learning algorithms Two application-oriented learning automata: one to discover and track spatiotemporal event patterns, and the other to solve stochastic searching on a line Demonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimization How to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probability A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.