Digital Twin Framework for Machine Learning-Enabled Integrated Production and Logistics Processes

Greis, Noel P.; Nogueira, Monica L.; Rohde, Wolfgang

doi:10.1007/978-3-030-85874-2_23

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Machine learning based prediction, optimization, and decision making enabled the quick response to the disruptions in production and logistics. 23 Negri et al 24 proposed a digital twin framework that can integrate a digital shadows simulation model with MES, the frameworks have been tested and validated in their Industry 4.0 laboratory. They design an intelligence layer with rules and knowledge to manage error states and trigger the real-world disassembly processes when low assembly quality is detected.…”

Section: Literature Reviewmentioning

confidence: 99%

The architecture evolution of intelligent factory logistics digital twin from planning, implement to operation

Qiu,

Chen,

Wang

et al. 2023

Advances in Mechanical Engineering

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Digital twin is widely studied in the context of industry 4.0. It is expected that the application of digital twin in intelligent factory logistics can enrich connectivity, proactivity, and agility of the logistics. Architecture is one of the significant factors impacting on the selection of appropriate enabling technologies for constructing the intelligent logistics. This paper proposes a digital twin architecture for intelligent factory logistics. It mainly includes a physical layer, two cyber layers, and an interface layer. The architecture is in compliance with the architecture of the Human-Cyber-Physical System (HCPS). Moreover, the evolution of the architecture is elaborated during the planning, implement, and operation stages when construction of the digital twin. At the initial stage, human should participate in the decision-making process frequently to determine whether the results given by digital twin need to be changed. However, the data-driven model based digital twin will continually learn the human’s changing behaviors, thus constantly updating itself. It can evolution from digital model, digital shadow to digital twin with the continuous construction process. The ultimate digital twin should be able to assess operational key performance indicators (KPIs) and handle dynamic events.

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Section: Literature Reviewmentioning

confidence: 99%

The architecture evolution of intelligent factory logistics digital twin from planning, implement to operation

Qiu,

Chen,

Wang

et al. 2023

Advances in Mechanical Engineering

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“…In terms of production logistics system research, existing studies focused on production equipment layout design, logistics scheduling, and process modeling (e.g., data flow diagram modeling, action diagram modeling) [14,28,29]. In terms of production logistics enabling technology applications, existing research mainly included equipment (e.g., Automated Guided Vehicle, AGV; Automatic Storage and Retrieval System, AS/RS), information systems (e.g., Enterprise Resource Planning, ERP; Warehouse Management System, WMS) and modern information technologies (e.g., Internet of Things, IoT; Digital Twin, DT) [30][31][32][33][34][35][36][37]. In general, the academic research results in the field of production logistics are fruitful and cover a wide range.…”

Section: Introductionmentioning

confidence: 99%

Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions

Zhang

et al. 2023

Systems

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At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and the high frequency of order changes. In order to systematically analyze the research status and dynamic evolution trend of production logistics in the Industry 3.X stage, this paper designed a Log-Likelihood ratio-based latent Dirichlet allocation (LLR-LDA) algorithm based on bibliometrics and knowledge graph technology, taking the literature of China National Knowledge Infrastructure and Web of Science database as the data source. In-depth bibliometric analysis of literature was carried out from research progress, hotspot evolution, and frontier trends. At the same time, taking the case of scientific research projects overcome by our research group as an example, it briefly introduced the synchronized decision-making framework of digital twin-enabled production logistics system. It is expected to broaden the research boundary of production logistics in the Industry 3.X stage, promote the development and progress of the industry, and provide valuable reference for steadily moving towards the Industry 4.0 stage.

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“…The relevance analysis here relies on the comparison of data flows, which is not always possible for other transport systems. Greis, et al [13] analyzes the Digital Twin Framework for integrated transportation and logistics processes with the support of machine learning, which is also a specific method. Kim, et al [14] studied the spatiotemporal density method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and control of data relevance in information systems of the transport industry management

Klius,

Prodanchuk,

Gololobova

et al. 2023

Nauk. visn. nat. hirn. univ.

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Purpose. Analysis of data coming to the information systems (IS) of the transport area (TA) and development of proposals for verification of their relevance to ensure reliability and efficiency of management. Development of the evaluation and control method of data relevance in information systems of the TA. Methodology. Special and general methods of scientific knowledge are used: critical analysis to establish scenarios of the impact of data relevance on the level of effective interaction of the elements of the system and its management; content analysis for stratification of requirements for data from IS and management decisions at the stage of analysis; mathematical formalization for the development of the data evaluation method and control of their relevance, which is proposed as the mathematical base of the algorithm; method of sequential selection for automatic detection of functional dependence, which describes data with a smaller value of standard error. Findings. Proposals to ensure data relevance have been developed. Data and management decision stratification has been proposed which will allow avoiding common errors due to non-normalization of data presentation. The scenarios of the impact of data relevance on the level of effective interaction of the elements of the transport system and its management have been established. For permanent data control, an algorithm for detecting deviations in consecutive data sets, automatic analysis of specified deviations, and detection of univariate and multivariate nonlinear patterns that cause deviations has been proposed. In case of inconsistencies of data with the identified non-linear patterns, the information is checked by the method of evolutionary programming. The effectiveness of the proposed methods is demonstrated using the example of analysis of the cargo turnover of seaports whose trends are characterized by significant changes over time and across ports. These trends may even have opposite characteristics. Originality. The method of data evaluation and control of their relevance, which is based on the methods of evolutionary (“genetic”) programming, is developed. Practical value. Proposals for evaluation and control of data relevance in information systems of the TA will increase the efficiency of management activities.

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Digital Twin Framework for Machine Learning-Enabled Integrated Production and Logistics Processes

Cited by 5 publications

References 23 publications

The architecture evolution of intelligent factory logistics digital twin from planning, implement to operation

The architecture evolution of intelligent factory logistics digital twin from planning, implement to operation

Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions

Evaluation and control of data relevance in information systems of the transport industry management

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