International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19
Published: May 14, 2025
Language: Английский
Applied Energy, Journal Year: 2022, Volume and Issue: 326, P. 119986 - 119986
Published: Sept. 29, 2022
Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, received increasing attention from the industry academia. As one most important applications for IoT, sustainable smart enables lower cost, higher productivity flexibility, better quality sustainability during product lifecycle management. Over years, numerous enterprises have promoted implementation both manufacturing. In Industry 4.0 context, a ‘digital twin’ is widely used to achieve manufacturing, although this approach often ignores sustainability. This study aims simultaneously consider digital twin big data technologies propose strategy based on information management systems energy-intensive industries (EIIs) perspective. The integration provides key acquisition in production environments, prediction mining uncertain environments as well real-time control complex working conditions. Moreover, twin-driven operation mechanism an overall framework cleansing are designed explain illustrate Two case studies Southern Northern China demonstrate efficacy strategy, with results showing that Companies A B achieved goals energy saving cost reduction after implementing proposed strategy. By applying system, unit consumption Company decreased by at least 3%. addition, ‘cradle-to-gate’ analysis indicates costs environmental protection decrease significantly. Finally, effectiveness some managerial insights EIIs analysed discussed.
Language: Английский
Citations
140
International Journal of Production Research, Journal Year: 2023, Volume and Issue: 62(6), P. 2220 - 2232
Published: May 31, 2023
Digital twins became of greater interest to researchers and practitioners in supply chain operations management (SCOM). Literature has addressed the need understand digital SCOM, mostly focusing on fragmented technological solutions use cases. We start with an integrative literature review determine which elements belong research SCOM. define seven major a twin SCOM: technology, people, management, organisation, scope, task, modelling. also distinguish five types product, process, network-of-networks. Illustration SCOM is provided using anyLogistix example. conclude that are not merely simulation-based replica real object but complex socio-technical phenomenon involved continuous human-artificial intelligence interactions. This leads understanding role through lens Industry 5.0, reconfigurable viable chains. Researchers alike can our framework structure knowledge consider all when designing twins.
Language: Английский
Citations
76
IEEE Transactions on Evolutionary Computation, Journal Year: 2023, Volume and Issue: 28(1), P. 147 - 167
Published: March 10, 2023
Job shop scheduling (JSS) is a process of optimizing the use limited resources to improve production efficiency. JSS has wide range applications, such as order picking in warehouse and vaccine delivery under pandemic. In real-world environment often complex due dynamic events, job arrivals over time machine breakdown. Scheduling heuristics, e.g., dispatching rules, have been popularly used prioritize candidates machines manufacturing make good schedules efficiently. Genetic programming (GP), shown its superiority learning heuristics for automatically flexible representation. This survey first provides comprehensive discussions recent designs GP algorithms on different types JSS. addition, we notice that years, techniques, feature selection multitask learning, adapted effectiveness efficiency heuristic design with GP. However, there no discuss strengths weaknesses these approaches. To fill this gap, article techniques automatic current issues challenges are discussed identify promising areas future.
Language: Английский
Citations
62
Materials & Design, Journal Year: 2024, Volume and Issue: 244, P. 113086 - 113086
Published: June 25, 2024
Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM and utilize it for optimizing various aspects such as process, supply chain, real-time monitoring. Data integration into proposed digital twin frameworks application machine learning techniques is expected play pivotal roles advancing future. In this paper, we provide an overview twin-assisted AM. On one hand, discuss domain highlight machine-learning methods utilized field, including material analysis, design optimization, process parameter defect detection monitoring, sustainability. other examine status current technical approach offer future developments perspectives area. This review paper aims present convergence big data, learning, Although there are numerous papers on additive others twins AM, no existing considered how these concepts intrinsically connected interrelated. Our first integrate three propose a cohesive framework they can work together improve efficiency, accuracy, sustainability processes. By exploring latest advancements applications within domains, our objective emphasize potential advantages possibilities associated with technologies
Language: Английский
Citations
48
Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e26503 - e26503
Published: Feb. 21, 2024
A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, system in the real world. It was first introduced to world by National Aeronautics and Space Administration (NASA) through its Apollo Mission '60s. can successfully design object from physical counterpart. However, main function twin provide bidirectional data flow between entity so that it continuously upgrade state-of-the-art iterative method for creating autonomous system. Data brain building block any The articles are found online cover individual field two at time regarding analysis technology. There no overall studies this manner online. purpose study overview level system, involves various phases. This paper will comparative among all fields which twins have been applied recent years. works with vast amount data, needs be organized, stored, linked, put together, also motive our study. essential models, making cyber-physical connections, running intelligent operations. current development status challenges present different phases discussed. outlines how DT used fields, like manufacturing, urban planning, agriculture, medicine, robotics, military/aviation industry, shows structure based on every sector using review papers. Finally, we attempted give horizontal comparison features across extract commonalities uniqueness sectors, shed light as well limitations future standpoint.
Language: Английский
Citations
34
Applied Energy, Journal Year: 2023, Volume and Issue: 337, P. 120843 - 120843
Published: March 1, 2023
Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, governance (ESG) can promote energy-intensive enterprises to achieve smart sustainable production. In Industry 4.0, various advanced technologies used manufacturing, but sustainability of production is often ignored without considering ESG performance. This study proposes a strategy edge-cloud cooperation-driven realise data collection, preprocessing, storage analysis. detail, kernel principal component analysis (KPCA) decrease interference abnormal in evaluation results. Subsequently, an improved technique for order preference similarity ideal solution (TOPSIS) based on adversarial interpretative structural model (AISM) proposed evaluate efficiency workshop make results more intuitive. Then, architecture models verified using real from partner company. Finally, discussed perspective economic impact, greenhouse gas emissions prevention.
Language: Английский
Citations
42
Journal of Manufacturing Systems, Journal Year: 2023, Volume and Issue: 70, P. 288 - 308
Published: Aug. 8, 2023
Language: Английский
Citations
36
Applied Energy, Journal Year: 2023, Volume and Issue: 349, P. 121608 - 121608
Published: July 31, 2023
In Industry 4.0, the production data obtained from Internet of Things has reached magnitude big with emergence advanced information and communication technologies. The massive low-value density challenges traditional clustering correlation analysis. To solve this problem, a data-driven analysis based on is proposed to improve energy resource utilisation efficiency in paper. detail, units abnormal energy-intensive consumption can be classified by using Additionally, feature extraction carried out same cluster migrated training set accuracy. Then, balance relationship between supply demand, which reduce carbon emission enhance sustainable competitiveness. sensitivity results show that method accuracy compared original model. conclusion, uncover potential product yield, thus improving resources.
Language: Английский
Citations
31
Robotics and Computer-Integrated Manufacturing, Journal Year: 2024, Volume and Issue: 89, P. 102778 - 102778
Published: May 4, 2024
Language: Английский
Citations
16
Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 74, P. 1037 - 1056
Published: May 25, 2024
Language: Английский
Citations
9