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Titlebook: Optimization and Data Science: Trends and Applications; 5th AIROYoung Worksh Adriano Masone,Veronica Dal Sasso,Valentina Morand Conference

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發(fā)表于 2025-3-21 17:48:17 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Optimization and Data Science: Trends and Applications
副標題5th AIROYoung Worksh
編輯Adriano Masone,Veronica Dal Sasso,Valentina Morand
視頻videohttp://file.papertrans.cn/704/703203/703203.mp4
概述Provides a fast point of entry into the most recent research topics on optimization and data science.Territorial and industrial systems can potentially benefit from the recent applications presented.I
叢書名稱AIRO Springer Series
圖書封面Titlebook: Optimization and Data Science: Trends and Applications; 5th AIROYoung Worksh Adriano Masone,Veronica Dal Sasso,Valentina Morand Conference
描述This proceedings volume collects contributions from the 5th AIRO Young Workshop and AIRO PhD School 2021 joint event on “Optimization and Data Science: Trends and Applications”, held online, from February 8 to 12, 2021. The joint event was organized by AIROYoung representatives and the Operations Research Group of the Department of Electrical Engineering and Information Technology of the University “Federico II” of Naples..The selected contributions represent the state-of-the-art knowledge related to different branches of research, such as data science, machine learning and combinatorial optimization. Therefore, this book is primarily addressed to researchers and PhD students of the operations research community. However, due to its interdisciplinary content, it will be of high interest for other closely related research communities.?Moreover, this volume not only presents theoretical results but also covers real applications in computer science, engineering, economics, healthcare, and logistics, making it interesting for practitioners facing complex decision-making problems in these areas..
出版日期Conference proceedings 2021
關鍵詞Operations Research; Mathematical Programming; Optimization; Management Science; Discrete Mathematics
版次1
doihttps://doi.org/10.1007/978-3-030-86286-2
isbn_softcover978-3-030-86288-6
isbn_ebook978-3-030-86286-2Series ISSN 2523-7047 Series E-ISSN 2523-7055
issn_series 2523-7047
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-22 00:16:25 | 只看該作者
2523-7047 potentially benefit from the recent applications presented.IThis proceedings volume collects contributions from the 5th AIRO Young Workshop and AIRO PhD School 2021 joint event on “Optimization and Data Science: Trends and Applications”, held online, from February 8 to 12, 2021. The joint event was
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Potential Sales Estimates of a New StoreFor this project, both a . model and a ., are used together..The aim is to support the sales managers engaging new PoS with an automatic tool, which in each year’s quarter examines all the Italian active commercial activities and returns the most promising ones.
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An Optimization Model for Service Requests Management in a 5G Network Architecturem for which we derive the associated Variational inequality formulation. Also, qualitative properties in terms of existence and uniqueness of solution are provided. Finally, a numerical example is performed to validate the effectiveness of the model.
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發(fā)表于 2025-3-22 16:16:35 | 只看該作者
Reinforcement Learning for the Knapsack Problemto achieve solutions for the knapsack problem, which is a CO problem. Our algorithm finds close to optimal solutions for instances up to one hundred items, which leads to conjecture that RL and self-attention may be major building blocks for future state-of-the-art heuristics for other CO problems.
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發(fā)表于 2025-3-22 20:21:31 | 只看該作者
Instance Generation Framework for Green Vehicle Routingiants are considered, (i) where consecutive AFSs visits are not allowed, and (ii) where consecutive AFSs visits are allowed. The results are analyzed and discussed, and conclusions on the benefits of the contributions are presented.
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Metal Additive Manufacturing: Nesting vs. Schedulingelationship between nesting and scheduling when planning and scheduling SLM machines. Numerical examples are conducted to show that optimal nesting does not guarantee optimal scheduling. It is concluded that the nesting and scheduling problems must be considered simultaneously to ensure feasibility.
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