الصفحة 1
الصفحة 1
Multicriteria Optimization
Decision makers in many areas, from industry to engineering and the social sector, face an increasing need to consider multiple, conflicting objectives in their decision processes. In many cases these real world decision problems can be formulated as multicriteria mathematical optimization models. The solution of such models requires appropriate techniques to compute so called efficient, or Pareto optimal, or compromise solutions that - unlike traditional mathematical programming methods - take the contradictory nature of the criteria into account. This book provides the necessary mathematical foundation of multicriteria optimization to solve nonlinear, linear and combinatorial problems with multiple criteria. Motivational examples illustrate the use of multicriteria optimization in practice. Numerous illustrations and exercises as well as an extensive bibliography are provided.
Frontiers in Computing Technologies for Manufacturing Applications
Frontiers in Computing Technologies for Manufacturing Applications presents an overview of the state-of-the-art intelligent computing in manufacturing. Modeling, data processing, algorithms and computational analysis of difficult problems found in advanced manufacturing are discussed. It is the first book to bring together combinatorial optimization, information systems and fault diagnosis and monitoring in a consistent manner. Techniques are presented in order to aid decision makers needing to consider multiple, conflicting objectives in their decision processes. In particular, the use of metaheuristic optimization techniques for multi-objective problems is discussed. Readers will learn about computational technologies that can improve the performance of manufacturing systems ranging from manufacturing equipment to supply chains.
Distributed artificial intelligence ; 2nd International conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI.
Discrete-Time Markov Chains : Two-Time-Scale Methods and Applications
The motivation stems from existing and emerging applications in optimization and control of complex hybrid Markovian systems in manufacturing, wireless communication, and financial engineering. Much effort in this book is devoted to designing system models arising from these applications, analyzing them via analytic and probabilistic techniques, and developing feasible computational algorithms so as to reduce the inherent complexity. This book presents results including asymptotic expansions of probability vectors, structural properties of occupation measures, exponential bounds, aggregation and decomposition and associated limit processes, and interface of discrete-time and continuous-time systems. One of the salient features is that it contains a diverse range of applications on filtering, estimation, control, optimization, and Markov decision processes, and financial engineering.
Decision-Making in Engineering Design : Theory and Practice
We use our brains when we create plans and designs. The resulting plans and designs take physical form, however, what we thought about, the alternatives we tried, and the constraints we recognized while we were making these plans and designs are usually not written anywhere. Therefore, those who only get to see the results, e.g. the final text and drawings, do not learn what led the designer to reach such conclusions and as a consequence never understand the real design. This description of decision processes will provide the means for the development of new manufacturing systems and production activities in the future because it helps us gain a real understanding of the how the mind processes we go through when making decisions affect the decisions that we make.
Markov Decision Processes with Their Applications
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Alternatives Considered But Not Disclosed : The Ambiguous Role of PowerPoint in Cross-Project Learning
This study investigates the role of PowerPoint in organizational communication, particularly in terms of a functional dilemma between its application for documentation as opposed to presentation purposes. The theoretical part of the analysis combines insights from both organizational communication studies (J. R. Taylor et al.) and social systems theory (N. Luhmann et al.). The empirical analysis shows that PowerPoint documents created for cross-project learning purposes contribute to an invisibilization rather than a visibilization of decision processes and their contingency.
