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Titlebook: Optimization and Decision Science: Methodologies and Applications; ODS, Sorrento, Italy Antonio Sforza,Claudio Sterle Conference proceeding

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发表于 2025-3-21 17:24:40 | 显示全部楼层 |阅读模式
书目名称Optimization and Decision Science: Methodologies and Applications
副标题ODS, Sorrento, Italy
编辑Antonio Sforza,Claudio Sterle
视频video
概述Presents cutting edge and rigorously developed papers in the field of optimization, decision science, problem solving methods, and their application in industrial and territorial systems.Highlights cr
丛书名称Springer Proceedings in Mathematics & Statistics
图书封面Titlebook: Optimization and Decision Science: Methodologies and Applications; ODS, Sorrento, Italy Antonio Sforza,Claudio Sterle Conference proceeding
描述This proceedings volume highlights the state-of-the-art knowledge related to optimization, decisions science and problem solving methods, as well as their application in industrial and territorial systems. It includes contributions tackling these themes using models and methods based on continuous and discrete optimization, network optimization, simulation and system dynamics, heuristics, metaheuristics, artificial intelligence, analytics, and also multiple-criteria decision making. The number and the increasing size of the problems arising in real life require mathematical models and solution methods adequate to their complexity. There has also been increasing research interest in Big Data and related challenges. These challenges can be recognized in many fields and systems which have a significant impact on our way of living: design, management and control of industrial production of goods and services; transportation planning and traffic management in urban and regional areas;energy production and exploitation; natural resources and environment protection; homeland security and critical infrastructure protection; development of advanced information and communication technologies
出版日期Conference proceedings 2017
关键词Optimization; Algorithms; Decision Methods; Operations Research Applications; Business Analytics; Emergen
版次1
doihttps://doi.org/10.1007/978-3-319-67308-0
isbn_softcover978-3-319-88410-3
isbn_ebook978-3-319-67308-0Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Look-Up Tables for Efficient Non-Linear Parameters Estimationusing the well-known Levenberg-Marquardt algorithm (LMA). Results show that the proposed approach and LMA are not statistically different in terms of MSE (with respect to the observed data) and in terms of Sum of Differences in the parameter and in the solution spaces. Considering the computational
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er Weiterempfehlung lediglich der erste Schritt, um die Anwendungsmöglichkeiten des Weiterempfehlungswert-Modells aufzuzeigen. Hierfür ist es zunächst erforderlich, Parameter für die unabhängigen Variablen in unseren Modellen zur Vorhersage von Abgabe und Einfluß von Weiterempfehlung zu ermitteln. D
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