Simulation-Based Optimization / Operations Research/Computer Science Interfaces Series Bd.55 (PDF)
58 DeutschlandCard Punkte sammeln
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.
Key features of this revised and improved Second Edition include:
· Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)
· Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics
· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata
· A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations
Themed around three areas in separate sets of chapters - Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis - this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics.
He has published more than fifty journal and conference articles - many of which have appeared in leading scholarly journals such as Management Science, Automatica, INFORMS Journal on Computing, Machine Learning, Journal of Retailing, Systems and Control Letters and the European Journal of Operational Research. He has also authored numerous book chapters on simulation-based optimization and operations research. His research has been funded by the National Science Foundation, Department of Defense, Missouri Department of Transportation, University of Missouri Research Board and industry. He has consulted extensively for the U.S. Department of Veterans Affairs and the mass media as a statistical/simulation analyst. He has received teaching awards from the Institute of Industrial Engineers.
He currently serves as an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology in Rolla, MO. He holds a masters degree in Mechanical Engineering from the Indian Institute of Technology and a Ph.D. in Industrial Engineering from the University of South Florida. He is a member of INFORMS, IIE and ASEE.
- Autor: Abhijit Gosavi
- 2014, 2nd ed. 2015, 508 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1489974911
- ISBN-13: 9781489974914
- Erscheinungsdatum: 30.10.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 5.10 MB
- Mit Kopierschutz
- Vorlesefunktion
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Simulation-Based Optimization / Operations Research/Computer Science Interfaces Series Bd.55".
Kommentar verfassen