Symbiotic Simulation System (S3) for Industry 4.0

Onggo, Bhakti Stephan

doi:10.1007/978-3-030-04137-3_10

Cited by 12 publications

(13 citation statements)

References 15 publications

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“…As discussed before, in many industries it is not possible to have an S3 without an appropriate EDSS. Our paper extends the earlier work from Onggo et al (2018) and Onggo (2019) by extending the architecture of S3 to include the interaction with an EDSS. We also add the latest developments in each of the S3 components and in particular we significantly extend the discussion on the machine learning component.…”

Section: Architecture Of a Symbiotic Simulation Systemmentioning

confidence: 59%

“…Earlier works (Onggo et al 2018;Onggo 2019) have identified the components of an S3, which includes a simulation module, a data acquisition component, an optimisation module, a machine-learning component, a data analytics module, and a scenario manager. However, the role of EDSS was not considered.…”

Section: Architecture Of a Symbiotic Simulation Systemmentioning

confidence: 99%

“…These symbiotic simulation systems (S3) need an EDSS that can manage the large volume of data gathered and transmitted via IoT-related technologies. For the case of an airport system, Figure 1 illustrates the relationship among these concepts, while Onggo (2019) provides a review of various terms used to describe a similar simulation system.…”

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confidence: 99%

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Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

Onggo

Corlu

Juan

et al. 2020

Enterprise Information Systems

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A symbiotic simulation system (S3), sometimes also called a 'digital twin', enables interactions between a physical system and its computational model representation. With the goal of supporting operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors. This real-time data is also saved in an enterprise data storage system (EDSS), so it can be used as historical data for future use. Both real-time and historical data are then used as inputs to the different components of an S3, which typically comprises several modules: data acquisition, simulation, optimisation, machine learning, and an 'actuator'. The latter is needed when there is not a human agent between the S3 and the system. Given the amount of data generated by today's smart systems, an S3 needs to be coupled with an EDSS. Furthermore, the S3 may produce a large amount of output data that needs to be stored, since it might be re-used by the machine learning module to make the S3 adaptive in dynamic scenarios. With the goal of supporting real-time operational decision-making -specially in Industry 4.0 applications such as smart cities, smart factories, intelligent transportation systems, and digital supply chains -, this paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. Moreover, the paper reviews the state-of-the-art in S3, and analyses how these systems can interact with EDSSs to make real-time decision making a reality. Finally, the paper also points out several research challenges in S3.

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Section: Architecture Of a Symbiotic Simulation Systemmentioning

confidence: 59%

Section: Architecture Of a Symbiotic Simulation Systemmentioning

confidence: 99%

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Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

Onggo

Corlu

Juan

et al. 2020

Enterprise Information Systems

Self Cite

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“…Our framework for hybrid modelling will increase the credibility and efficacy of conjoined approaches for future research, including but not limited to M&S of the next generation of the IoT (D'Angelo et al 2019), edge and fog computing ( Gupta et al 2017 ) and symbiotic simulation for Industry 4.0 ( Onggo 2019 ). These cross-disciplinary efforts require conceptualisations and toolsets that are no longer based on methods resulting from the era of reductionism, but require holistic views that HMs can provide.…”

Section: Discussionmentioning

confidence: 99%

Hybrid models as transdisciplinary research enablers

Tolk

Harper

Mustafee

2021

European Journal of Operational Research

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“…We apply the paradigm of a Dynamic Data Driven Application System (DDDAS) [21], [22] to support the close coupling that is required between a real system and the virtual representation. In a DDDAS, sensor data and the simulation model are closely coupled in a real-time feedback loop to reflect the environment as it evolves in real time [23]. Currently there is limited research looking at applying digital twin for modelling biological processes or systems in which man made and natural systems interact such as farming.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Urban Agriculture Using Digital Twin: A Case Study With Aquaponics

et al. 2021

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There are many pressures on the global food system such as urbanization, climate change, and environmental degradation. Urban agriculture is an approach to producing food inside cities where, globally, more than half the worlds population live. It has been shown to have a range of potential benefits, for instance in reducing waste and logistics costs. Increased uptake of urban farming can even relieve pressure on the natural environment by reducing the burden of production required from farmland by creating space for it to recover from accumulated damage as a result of the use of unsustainable farming practices historically. This article describes an approach for a new type of decision support system suitable for urban farming production. We discuss differences between the requirements and the users of decision support in urban agriculture, and those of ordinary agribusiness enterprises. A case study is performed using a novel technology for urban farming: a cyber-physical implementation of aquaponics is enhanced with adaptive capabilities using a digital twin system and machine learning. Aquaponics is a farming technique that utilizes a harmonious nutrient exchange cycle for growing plants and fish together, while conserving water, and possibly without the need for soil or even sunlight. Empirical results are provided that evaluate the use of data driven decision analytics and a digital twin model to plan production from the aquaponic system during a three month trial. Another set of results evaluate a proposed modelling framework for large scale urban agriculture ecosystems. This concept forms the basis of the suggested approach for an urban farming decision support system that coordinates the activities of many independent producers to target collective goals.INDEX TERMS Decision support, urban agriculture, modelling, simulation, digital twin, Internet of Things, metaheuristics.

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Symbiotic Simulation System (S3) for Industry 4.0

Cited by 12 publications

References 15 publications

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

Hybrid models as transdisciplinary research enablers

A Decision Support System for Urban Agriculture Using Digital Twin: A Case Study With Aquaponics

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