A digital twin-based flexible cellular manufacturing for optimization of air conditioner line

Guo, Hongfei; Chen, Minshi; Khalgui, Mohamed; Qu, Ting; Wang, Siming; Li, Jianke

doi:10.1016/j.jmsy.2020.07.012

Cited by 75 publications

(31 citation statements)

References 20 publications

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“…Ozden et al (2021) present a computational software system using a meta-heuristic for facilitating the design of warehouse layouts to near-optimality, considering the average walking distance of the picker. Guo et al (2021) put forward a method of flexible cellular manufacturing based on digital twin. It continuously optimizes the method, based on decoupling event mechanisms and multi-objective constraints.…”

Section: Solution Methods and Toolsmentioning

confidence: 99%

Layout optimization methods and tools: A systematic literature review

Chemim¹,

Sotsek²,

Kleina³

2021

GEPROS

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Purpose: This article aims to show the methods used to optimize layout and tools that have been applied since 2010 in the most diverse production environments. Theoretical Reference: Due to the intense competitiveness and uncertainties in the current market, improving processes and increasing production efficiency by managing the layout of a facility can be one of the methods to benefit organizations. In this context, knowledge of the main tools and methods is essential in defining the conduct of studies. Design/methodology/approach: Research was carried out through a systematic literature review. The databases used were Science Direct and Portal Capes. Using keywords, a defined scope and the relevance of the theme, international articles were selected for reading and discussion. Findings: Through the review, it was possible to select 51 articles which were relevant to the topic. Due to the complexity of the layout management study, it was found that increasingly more algorithms, mathematical and computational models are being used to solve these NP-hardness problems. Research, Practical & Social implications: adjusting to new methods and ways of solving problems laying out facilities, although scenarios can be extremely varied, they can improve business results by making the process more efficient. Originality/value: The article compiles and briefly explains the methods found to optimize the layout, which is of great importance considering the knowledge that these models spread on production management practices. Keywords: Layout optimization. Algorithm. Method. Facility layout planning

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Section: Solution Methods and Toolsmentioning

confidence: 99%

Layout optimization methods and tools: A systematic literature review

Chemim¹,

Sotsek²,

Kleina³

2021

GEPROS

View full text Add to dashboard Cite

show abstract

“…Within the various presented concepts and frameworks [ 34 , 35 , 36 , 37 , 38 , 39 ], automated production systems, including mixed reality assistance systems [ 40 , 41 ], could be rapidly modularized [ 42 ] and reconfigured [ 43 , 44 , 45 ], enhanced with AI [ 46 , 47 ] and sensors [ 48 , 49 ] and, in combination with cloud and edge computing [ 50 ], transformed into distributed control systems, while detailed production environments can be generated and updated in the form of 3D point clouds [ 51 , 52 , 53 , 54 , 55 , 56 ]. Based on these infrastructures, DT demonstrates the capability of handling increasingly complex operational problems, such as production planning and scheduling [ 57 , 58 , 59 , 60 ], production monitoring and control [ 61 , 62 , 63 , 64 , 65 , 66 ], quality control and management [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], as well as logistics [ 76 , 77 , 78 ], supply chain management (SCM) [ ...…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

151

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…Zhang et al [69] first discussed a common structure of DT-based cyber-physical manufacturing system and developed a cyber-physical production system on the basis of AutomationML, which was eventually evaluated for blisk machining. Liu et al [70] also developed a DT-based cyber-physical manufacturing system by combining cyber-physical system, DT modeling and simulation techniques, event-driven distributed collaboration mechanisms, networking techniques, and cloud computing, which could ensure the easy operation of the DT-based cyber-physical manufacturing system.…”

Section: Cyber-physical Production Systemmentioning

confidence: 99%

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

Yang¹,

Karimi²,

Kaynak³

et al. 2021

CES

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Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.

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A digital twin-based flexible cellular manufacturing for optimization of air conditioner line

Cited by 75 publications

References 20 publications

Layout optimization methods and tools: A systematic literature review

Layout optimization methods and tools: A systematic literature review

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

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