Author: Jürgen Guddat
Publisher:
ISBN:
Category : Mathematical analysis
Languages : en
Pages : 184

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Author: Collet's Holdings, Ltd. Staff
Publisher:
ISBN: 9780785511816
Category :
Languages : en
Pages :

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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 175

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Author: Jürgen Guddat
Publisher:
ISBN:
Category :
Languages : en
Pages : 175

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Author: Ursula Sebastian
Publisher:
ISBN:
Category :
Languages : en
Pages : 109

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Author: Conference on System Modelling and Optimization
Publisher:
ISBN:
Category :
Languages : en
Pages : 169

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Author: Abhijit Gosavi
Publisher: Springer
ISBN: 1489974911
Category : Business & Economics
Languages : en
Pages : 530

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Book Description
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce 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.

Author: Masatoshi Sakawa
Publisher: Springer Science & Business Media
ISBN: 144198402X
Category : Business & Economics
Languages : en
Pages : 268

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Book Description
Although studies on multiobjective mathematical programming under uncertainty have been accumulated and several books on multiobjective mathematical programming under uncertainty have been published (e.g., Stancu-Minasian (1984); Slowinski and Teghem (1990); Sakawa (1993); Lai and Hwang (1994); Sakawa (2000)), there seems to be no book which concerns both randomness of events related to environments and fuzziness of human judgments simultaneously in multiobjective decision making problems. In this book, the authors are concerned with introducing the latest advances in the field of multiobjective optimization under both fuzziness and randomness on the basis of the authors’ continuing research works. Special stress is placed on interactive decision making aspects of fuzzy stochastic multiobjective programming for human-centered systems under uncertainty in most realistic situations when dealing with both fuzziness and randomness. Organization of each chapter is briefly summarized as follows: Chapter 2 is devoted to mathematical preliminaries, which will be used throughout the remainder of the book. Starting with basic notions and methods of multiobjective programming, interactive fuzzy multiobjective programming as well as fuzzy multiobjective programming is outlined. In Chapter 3, by considering the imprecision of decision maker’s (DM’s) judgment for stochastic objective functions and/or constraints in multiobjective problems, fuzzy multiobjective stochastic programming is developed. In Chapter 4, through the consideration of not only the randomness of parameters involved in objective functions and/or constraints but also the experts’ ambiguous understanding of the realized values of the random parameters, multiobjective programming problems with fuzzy random variables are formulated. In Chapter 5, for resolving conflict of decision making problems in hierarchical managerial or public organizations where there exist two DMs who have different priorities in making decisions, two-level programming problems are discussed. Finally, Chapter 6 outlines some future research directions.

Author: Jürgen Guddat
Publisher: Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften
ISBN:
Category : Mathematics
Languages : en
Pages : 576

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Book Description
This volume contains the proceedings of the third conference on Parametric Optimization and Related Topics, held in Gustrow from 30 August until 5 September, 1991. Parametric optimization, as a part of mathematical programming, investigates the behaviour of solutions to optimization problems under data pertubations. This behaviour, like continuity and differentiability, plays a fundamental role for a series of further questions that are of interest from a practical as well as a theoretical point of view. Many relations to other disciplines of operations research, like stochastic programming, modelbuilding, numerical methods, multiobjective optimization and optimal control, originate from this behaviour. The presented articles (all refereed) are topical and original papers reflecting recent results to current directions of research in theory and applications."

Author: Jürgen Guddat
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 208

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Book Description
Explores optimization problems in which some or all of the individual data involved depends on one parameter. Beginning with a preliminary survey of solution algorithms in one-parametric optimization, the text moves on to examine the pathfollowing curves of local minimizers, pathfollowing along a connected component in the Karush-Kuhn-Tucker set and in the critical set, pathfollowing in the set of local minimizers and in the set of critical points. In addition, practical applications are included.