Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system

Leng, Jiewu; Yan, Douxi; Liu, Qiang; Zhang, Hao; Zhao, Guimin; Wei, Lijun; Zhang, Ding; Yu, Ao; Chen, Xin

doi:10.1080/0951192x.2019.1667032

Cited by 136 publications

(77 citation statements)

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“…On the basis of digital twin technology [21,22], the bidirectional mapping and real-time data interaction are constructed between physical entity and digital model. erefore, the comprehensive data integration and fusion of physical entity and digital model can be realized and form the digital twin data that can support and drive the green performance evaluation methodology.…”

Section: Overall Frameworkmentioning

confidence: 99%

“…By creating the virtual model of physical entity in a digital way and simulating the behavior of physical entity by means of data, digital twin has the characteristics of realtime synchronization, faithful mapping and high fidelity through the means of virtual real interaction feedback, data fusion analysis, and decision iteration selection optimization [21]. Digital twin can promote the interaction and integration of physical world and information world and increase or expand new capabilities for physical entity [22]. In this study, digital twin mainly focuses on obtaining the virtual image of all factors in the whole life cycle of intelligent manufacturing project to meet the monitoring and simulation requirements of the evaluation information source and demand for the green evaluation of intelligent manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, digital twin mainly focuses on obtaining the virtual image of all factors in the whole life cycle of intelligent manufacturing project to meet the monitoring and simulation requirements of the evaluation information source and demand for the green evaluation of intelligent manufacturing. Based on digital twin technology [21,22], the complete and dynamic mapping interaction between physical entity and digital model in green performance evaluation of intelligent manufacturing can be realized. And then, how to comprehensively master the multidimensional influencing factors and their coupling relationship for comparative analysis is the key problem in green performance evaluation of intelligent manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II

et al. 2020

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The design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently. Environmental and social factors should be systematically integrated into the life cycle of intelligent manufacturing. In view of this, a green performance evaluation methodology of intelligent manufacturing driven by digital twin is proposed in this paper. Digital twin framework, which constructs the bidirectional mapping and real-time data interaction between physical entity and digital model, provides the green performance evaluation with a total factor virtual image of the whole life cycle to meet the monitoring and simulation requirements of the evaluation information source and demand. Driven by the digital twin framework, a novel hybrid MCDM model based on fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II is proposed as the methodology for the green performance evaluation of intelligent manufacturing. The model is tested and validated on a study of the green performance evaluation of remote operation and maintenance service project evaluation for an air conditioning enterprise. Testing demonstrates that the proposed hybrid model driven by digital twin can enable a stable and reasonable evaluation result. A sensitivity analysis was carried out by means of 27 scenarios, the results of which showed a high degree of stability.

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Section: Overall Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II

et al. 2020

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“…Digital twin is considered to be the key technology in the future and has been widely concerned [15][16][17][18]. By digital twin, the virtual model of physical entity is created in a digital way and the behavior of physical entity is simulated by means of data.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

et al. 2020

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As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.

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“…• An introduction to IoT [47] and its history, from machine-to-machine (M2M) system solutions to IoT solutions [48][49][50] [56].…”

Section: Introduction To Internet Of Thingsmentioning

confidence: 99%

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Benis

Nelke

Winokur

2021

Sensors

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Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.

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Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system

Cited by 136 publications

References 50 publications

Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II

Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

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