Decentralized decision support for intelligent manufacturing in Industry 4.0

Marques, Maria; Agostinho, Carlos; Zacharewicz, Grégory; Jardim‐Gonçalves, Ricardo

doi:10.3233/ais-170436

Cited by 108 publications

(66 citation statements)

References 39 publications

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“…Industry 4.0 is known as the future of smart manufacturing and industrial revolution. Making decentralized decision is critical in Industry 4.0 (Marques et al, 2017). Horizontal and vertical integrations are two principal characteristics in Industry 4.0.…”

Section: Discussionmentioning

confidence: 99%

A discussion of objective function representation methods in global optimization

Pardalos¹,

Fathi²

2018

Front. Eng

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Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization (GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov's superposition and its application in GO. Finally, we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.

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Section: Discussionmentioning

confidence: 99%

A discussion of objective function representation methods in global optimization

Pardalos¹,

Fathi²

2018

Front. Eng

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“…The proposed framework is a brainstorming attempt to orient the EPDM research community to get fully involved towards activating this paper's future vision of more interactive and intelligent next-generation strategic EPDM solutions as it is the case within other disciplines such as (intelligent sustainable) manufacturing and Industry 4.0 [238,239], (green) supply chain management [240,241], and more significantly in (participative and intelligent) healthcare and medical decision support [242,243]. Thus, all involved energy planning stakeholders' are expected to express their feedbacks, agreements/disagreements, and more importantly their concerns for enhanced, sustainability-oriented strategic EPDM.…”

Section: Discussionmentioning

confidence: 99%

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

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Achieving sustainable energy planning and development involves complex decision-making processes. The energy planning decision-making (EPDM) field relies on a plethora of decision analysis methods that offered many solutions to process a variety of energy management and strategic decision-making problems. However, current EPDM solutions are unable to overcome the increasing complexity of strategic energy planning situations involving a large number of stakeholders in uncertain, dynamic, and distributed environments. This raises significant new challenges for researchers in both decision sciences and renewable and sustainable energy planning. On the basis of a representative assortment of peer-reviewed related literature selected by querying multiple electronic databases and indexed in Scopus and Web of Science databases domain journals over the last 12 years, this paper exhaustively highlights and discusses limitations of existing strategic EPDM solutions. The analysis is based on a classification specially developed by holistically harmonizing important domain concepts to categorize the considered representative sample of the field of interest. Additionally, this paper integrates results and conclusions from some recent and most cited literature reviews to (i) formulate essential evidence as well as practical and conclusive literature's supportalongside with the formulated representative sample -to this paper's subsequent insights and statements, and (ii) guarantee that no relevant articles have been excluded. A total of 78 related works is gathered and analyzed to provide a general view and discussion on major complexities found in classical/traditional strategic EPDM solutions and challenges for next-generation EPDM solutions. Moreover, a comparative analysis of the two solutions and a set of "quality indexes" of a next-generation EPDM solution were identified and some proposals were made to improve future applicative research. As an original result coming from the "quality indexes" identified through the review process, an intelligent expert-based framework for next-generation EPDM solutions is developed for enhanced renewable and sustainable energy planning.

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“…[18] Trying to achieve the I4.0 vision, blockchain will develop into a new type of manufacturing system in which supply chains will become demand chains and the entire system will become distributed. [28]…”

Section: J Blockchain In the Value Chainmentioning

confidence: 99%

BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

2019

IJEAT

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Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.

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Decentralized decision support for intelligent manufacturing in Industry 4.0

Cited by 108 publications

References 39 publications

A discussion of objective function representation methods in global optimization

A discussion of objective function representation methods in global optimization

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

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