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Titlebook: Scheduling in Industry 4.0 and Cloud Manufacturing; Boris Sokolov,Dmitry Ivanov,Alexandre Dolgui Book 2020 Springer Nature Switzerland AG

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書(shū)目名稱Scheduling in Industry 4.0 and Cloud Manufacturing
編輯Boris Sokolov,Dmitry Ivanov,Alexandre Dolgui
視頻videohttp://file.papertrans.cn/862/861363/861363.mp4
概述It uncovers fundamental principles and recent developments in the theory and application of scheduling methodology to cloud manufacturing and Industry 4.0.Bridging the scheduling theory to cloud manuf
叢書(shū)名稱International Series in Operations Research & Management Science
圖書(shū)封面Titlebook: Scheduling in Industry 4.0 and Cloud Manufacturing; Boris Sokolov,Dmitry Ivanov,Alexandre Dolgui Book 2020 Springer Nature Switzerland AG
描述This book has resulted from the activities of IFAC TC 5.2 “Manufacturing Modelling for Management and Control”. The book offers an introduction and advanced techniques of scheduling applications to cloud manufacturing and Industry 4.0 systems for larger audience. This book uncovers fundamental principles and recent developments in the theory and application of scheduling methodology to cloud manufacturing and Industry 4.0..The purpose of this book is to present recent developments in scheduling in cloud manufacturing and Industry 4.0 and to systemize these developments in new taxonomies and methodological principles to shape this new research domain. This book addresses the needs of both researchers and practitioners to uncover the challenges and opportunities of scheduling techniques’ applications to cloud manufacturing and Industry 4.0. For the first time, it comprehensively conceptualizes scheduling in cloud manufacturing and Industry 4.0 systems as a new research domain. The chapters of the book are written by the leading international experts and utilize methods of operations research, industrial engineering and computer science. Such a multi-disciplinary combination is unique
出版日期Book 2020
關(guān)鍵詞Scheduling; Optimal Control; Industry 4; 0; Deterministic Maximum Principle; Control Models; Smart Manufac
版次1
doihttps://doi.org/10.1007/978-3-030-43177-8
isbn_softcover978-3-030-43179-2
isbn_ebook978-3-030-43177-8Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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The Tolerance Scheduling Problem in a Single Machine Case,f the Tolerance Scheduling problem as well as the technical requirements of their embodiment in Industry 4.0’s DSS. We illustrate these ideas in a case study with a single machine, in which we analyze the performance of the model at different tolerances.
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Adaptive Scheduling in the Era of Cloud Manufacturing,gh market reshaping. Scheduling is a key field of manufacturing systems. Academia and industry are closely collaborating for producing enhanced solutions, taking advantage of multiple criteria. Initially, the scheduling problem was dealt with more simplistic methods resulting in static solutions; ho
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Coupling Robust Optimization and Model-Checking Techniques for Robust Scheduling in the Context of Research models for robust optimization and Discrete Event Systems models and tools for property checking. The idea is to iteratively call Operations Research and Discrete Event Systems Models for converging towards a solution with the required robustness level defined by the decision-maker.
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Integrated Scheduling of Information Services and Logistics Flows in the Omnichannel System,rvice-oriented description makes it possible to coordinate the information services and material process schedules simultaneously. It also becomes possible to determine the volume of information services needed for physical supply processes. In addition, impact of disruptions in information services
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A Generic Decision Support Tool to Planning and Assignment Problems: Industrial Applications and Inactivities or assign resources; operational to schedule activities. We present a generic and modular decision support tool to solve different problems of planning, assignment, scheduling, or lot-sizing. Our tool uses a hybridization between a metaheuristic and a list algorithm. The specification of
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