الصفحة 1
الصفحة 1
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
In this book we strive to provide models that capture many of the - tails faced by ?rms operating in a modern supply chain, but we stop short of proposing models for economic analysis of the entire multi-player chain. In other words, we produce models that are useful for planning within a supply chain rather than models for planning the supply chain. The usefulness of the models is enhanced greatly by the fact that they have been implemented - ing computer modeling languages. Implementations are shown in Chapter 7, which allows solutions to be found using a computer.
Formal Methods for Open Object-Based Distributed Systems ; Vol. 4037 ; 8th IFIP WG 6.1 International Conference, FMOODS 2006, Bologna, Italy, June 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the 8th IFIP WG 6.1 International Conference on Formal Methods for Open Object-Based Distributed Systems, FMOODS 2006, held in Bologna, Italy, June 2006. The book presents 16 revised full papers together with an invited paper and abstracts of 2 invited talks. Coverage includes component- and model-based design, service-oriented computing, software quality, modeling languages implementation, formal specification, verification, validation, testing, and service-oriented systems.
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.
