Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing netw...

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Other Authors: Puigjaner, Luis (Editor), Espuña Camarasa, Antonio (Editor), Muñoz Mata, Edrisi (Editor), Capón García, Elisabet (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
IoT
n/a
Online Access:DOAB: download the publication
DOAB: description of the publication
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700 1 |a Espuña Camarasa, Antonio  |4 edt 
700 1 |a Muñoz Mata, Edrisi  |4 edt 
700 1 |a Capón García, Elisabet  |4 edt 
700 1 |a Puigjaner, Luis  |4 oth 
700 1 |a Espuña Camarasa, Antonio  |4 oth 
700 1 |a Muñoz Mata, Edrisi  |4 oth 
700 1 |a Capón García, Elisabet  |4 oth 
245 1 0 |a Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools 
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300 |a 1 electronic resource (446 p.) 
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520 |a This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems. 
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546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a manufacturing process 
653 |a additive manufacturing 
653 |a IoT 
653 |a computer systems and networks 
653 |a 3D printing 
653 |a digital twin 
653 |a Industry 4.0 
653 |a pharmaceutical manufacturing 
653 |a biopharmaceutical manufacturing 
653 |a process modeling 
653 |a mixed-integer linear programming 
653 |a bin-packing problem 
653 |a material requirements planning 
653 |a agri-supply chain management 
653 |a variable production rate 
653 |a optimal resources 
653 |a imperfect production 
653 |a eco-efficient production 
653 |a closed-loop scheduling 
653 |a scheduling stability 
653 |a optimal control and scheduling 
653 |a fouling 
653 |a heat exchanger networks 
653 |a continuous manufacturing 
653 |a bioprocessing 
653 |a process systems engineering 
653 |a single-use technology 
653 |a AGV-Automated Guided Vehicles 
653 |a DES-Discrete Event Simulation 
653 |a FMS-Flexible Manufacturing Systems 
653 |a OEE-Overall Equipment Efficiency 
653 |a WCLcWorld Class Logistic 
653 |a abrasive water jet 
653 |a cutting 
653 |a surface quality 
653 |a quality prediction 
653 |a 3D simulation modeling and analysis 
653 |a model implementation 
653 |a bottleneck analysis 
653 |a production costs 
653 |a resource conservation 
653 |a smart manufacturing 
653 |a edge computing 
653 |a machine learning 
653 |a blockchain 
653 |a Industrial Internet of Things 
653 |a inventory management 
653 |a supply chain 
653 |a multi-echelon 
653 |a stochastic programming 
653 |a reinforcement learning 
653 |a digitalisation 
653 |a model-based 
653 |a computational engineering 
653 |a process simulation 
653 |a LNG terminal 
653 |a operational optimization 
653 |a BOG compressor 
653 |a MINLP 
653 |a enterprise process architecture 
653 |a new technologies integration 
653 |a process intelligence 
653 |a real-time monitoring 
653 |a fault detection 
653 |a predictive process adjustment 
653 |a vacuum gripper 
653 |a sensor data 
653 |a Tri-X Intelligence 
653 |a cyber-physical systems 
653 |a human-cyber systems 
653 |a intelligent systems 
653 |a intelligent manufacturing 
653 |a multi-parametric programming 
653 |a explicit MPC 
653 |a enterprise-wide optimisation 
653 |a set-point tracking 
653 |a algebraic geometry 
653 |a continuous pharmaceutical manufacturing 
653 |a model predictive control 
653 |a state estimation 
653 |a quality-by-control (QbC) 
653 |a glidant effects 
653 |a plant-model mismatch 
653 |a regional logistics 
653 |a low-carbon economy 
653 |a cloud model 
653 |a comprehensive evaluation 
653 |a Beijing-Tianjin-Hebei region 
653 |a pharmaceutical manufacture 
653 |a uncertainty 
653 |a operational flexibility 
653 |a operational envelopes 
653 |a modeling 
653 |a n/a 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/91192  |7 0  |z DOAB: description of the publication