Introduction to Personalisation in Cyber-Physical-Social Systems

Yilma, Bereket Abera; Naudet, Yannick; Panetto, Hervé

doi:10.1007/978-3-030-11683-5_3

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…It shows that the main objective functions for scheduling are reducing cost and meeting due date. Supply chain scheduling X X X X DP [20] Network for services X X X Ant algorithm [21] CPSS X X MOO [22] Smart Manufacturing X X KBE [23] Manufacturing X X 5-layer framework [24] production /fabrication X X NP hard [25] Manufacturing X X X NP [26] Cellular Manufacturing X X X Multi-agent System [27] Cell Formation X hierarchical GA [28] Fabrication & Ass.…”

Section: Scheduling For Mass Customization and For Cellular Assemblymentioning

confidence: 99%

Distributed Scheduling in Cellular Assembly for Mass Customization

Maalouf¹,

Duigou²,

Hussein³

et al. 2020

Product Lifecycle Management Enabling Smart X

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Industry 4.0 has many objectives; among them is increasing flexibility in manufacturing, as well as offering mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. In Mass Customization, manufacturers are challenged to produce customized products at the lowest possible cost with minimal lead-time. This increased customization increases complexity in production planning. The main challenge becomes planning production for lots of one and for high product variety and volatile market demand. Moreover, the customer requires real time update on his order status, and is less tolerant for delays. Nevertheless, in a make to order or assembly to order supply chain, many disturbances (supplier delay, machine brake-downs, transportation network disturbance, …) may highly increase the customer order delay. Hence, production planning in this context becomes more complex requiring real-time information exchange with all stages of the supply chain. This paper tries to answer this challenge by proposing a distributed production scheduling approach for mass customization in a cellular assembly layout.

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Section: Scheduling For Mass Customization and For Cellular Assemblymentioning

confidence: 99%

Distributed Scheduling in Cellular Assembly for Mass Customization

Maalouf¹,

Duigou²,

Hussein³

et al. 2020

Product Lifecycle Management Enabling Smart X

View full text Add to dashboard Cite

show abstract

“…CPS refers to a generation of systems with integrated computational and physical capabilities strongly related to the 4-th Industrial Revolution [1]. The Social aspect refers to interacting individuals, having each their own cognition, preferences, motivation and behaviour [2,4]. The development of CPSS is still in its infancy.…”

Section: Cyber-physical-social System (Cpss)mentioning

confidence: 99%

“…In an effort to address this gap, recently a holistic definition and domain -independent conceptualisation of CPSS grounded on a generic framework provided by the theory of system was proposed by [2]. In our work, we adopt a perspective on CPSS represented by the following definition.…”

Section: Cyber-physical-social System (Cpss)mentioning

confidence: 99%

A Meta-Model of Cyber-Physical-Social System: The CPSS Paradigm to Support Human-Machine Collaboration in Industry 4.0

Yilma

Panetto

Naudet

2019

IFIP Advances in Information and Communication Technology

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The 4 th industrial revolution (Industry 4.0) heavily relying on the concept of Cyber-Physical Systems (CPS) has transformed the manufacturing industry into an intelligent environment. Advances in manufacturing and automation industries created hyper-connected industrial ecosystems that are not limited to smart production but also facilitate organizational integration. Hence, fostering the creation of collaborative, networked and intelligent industries. One of the emerging advances in the digital transformation of industries is the creation of environments where humans work in close collaboration with sensor enabled smart machines and robots. Particularly the close involvement of humans in such smart environments challenges system designed methodologies mainly because human aspects are not considered in CPS design frameworks. In this paper, we present an approach to support this aspect of Industry 4.0 taking a Cyber-Physical-Social System (CPSS) paradigm to incorporate human aspects with the existing notion of CPS. We propose a meta-model of CPSS that can serve as a framework to design systems involving human and CPS collaboration.

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“…There are several methods in which the human actors used in CPS and hence getting popularity. This acts as the foundation of cyber-physical-social systems (CPSSs) [14]. CPSSs use data on social behavior, and perform relationship analysis to provide relevant information services [11].…”

Section: Introductionmentioning

confidence: 99%

Hotspots Analysis Using Cyber-Physical-Social System for a Smart City

Amin

Choi

2020

IEEE Access

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Internet of things plays a vital role in providing various services to users. Significant volumes of data are generated from the communication between a large numbers of heterogeneous devices over the Internet. Big data technology is generally used to handle the large volume of data. Complex networks are graphs (networks) having non-trivial topological features, such as random graphs and lattices. Big data of complex networks concerns big data methods that can be used to analyze massive structural data sets, including considerably large networks and sets of graphs. This study is based on the critical phenomenon arising in complex networks that enable us to analytically predict the hotspots in smart cities. Hotspots are places with significantly high communication traffic relative to others. In this study, we propose a cyber-physical-social system for the analysis of high communication traffic hotspots using telecom data. The proposed model constructs a graph, and perform social network analysis on it. The process of hotspot extraction is performed, followed by social network analysis, which is conducted by quantifying the importance of each hotspot based on network metrics. These metrics aid in determining the importance of each hotspot in a telecom data network. Our objective is to prioritize different areas and detect hotspots quickly. Our results indicate that the proposed model has an efficiency comparable with that of state of the art methods. This research study will be helpful for urban planning and development, as well as in upgrading telecommunication infrastructure.

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Introduction to Personalisation in Cyber-Physical-Social Systems

Cited by 9 publications

References 18 publications

Distributed Scheduling in Cellular Assembly for Mass Customization

Distributed Scheduling in Cellular Assembly for Mass Customization

A Meta-Model of Cyber-Physical-Social System: The CPSS Paradigm to Support Human-Machine Collaboration in Industry 4.0

Hotspots Analysis Using Cyber-Physical-Social System for a Smart City

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