Integration of industrial IoT architectures for dynamic scheduling

Coito, Tiago; Firme, Bernardo; Martins, Miguel S.E.; Costigliola, Andrea; Lucas, Rafaël; Figueiredo, João; Vieira, Susana M.; Sousa, J.M.

doi:10.1016/j.cie.2022.108387

Cited by 11 publications

(3 citation statements)

References 60 publications

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“…Also, a mixed integer linear programming extended with a Lagrange relation method is used [20] for energy-efficient scheduling under time-of-use pricing. The integration of industrial IoT with operational technologies to support the dynamic scheduling and rescheduling operations in a personalized production environment is proposed in [21]. Such approaches can enable data-driven decision-making and control facilitating the adoption of proactive strategies for a resilient and sustainable production system [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

2022

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This work describes an approach for the Digital Transformation (DT) of a manufacturing SME in the mold production industry. The phases for changing from manual and non-adding value labor-intensive practices to digital and smart manufacturing configurations are described. Initially, the needs of the SME are presented in terms of improving the planning and monitoring capabilities. Following the requirements analysis, a set of digital functionalities are proposed and mapped to the specific needs. The digital solution presented includes technologies such as the Internet of Things, data management, intelligent production planning and web technologies. The industrial pilot case has showed how the use of open-source off-the-shelf technology, integrated with the facility’s legacy systems can reduce daily production management tasks and ease the burden of planning and monitoring. The engineers and management team of the SME have acquired first-hand experience on the benefits digitalization of process can bring to the organization that is valuable for proceeding with the DT of the organization.

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Section: Literature Reviewmentioning

confidence: 99%

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

2022

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“…Wang 3 established a hybrid energy system model, including the shore power load prediction model, the shore power dynamic price model, the fan model and the energy storage model, which improved the problem of large error of the open-loop optimization method in an uncertain environment. Many studies discussed the energy system and transfer strategy of shore power [4][5][6][7][8] . However, there are still some deficiencies in these researches, which can not solve the problems of shore power load management and the suppression of inrush current of distribution transformer.…”

Section: Status Of Shore Power Technology At Home and Abroadmentioning

confidence: 99%

An intelligent shore power device controlled by Internet of Things

Liu

Zheng

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Taking the opportunity of the construction of shore power coverage at Huzhou public ports, this paper studies the shore-based power supply technology for ships in inland ports, aiming to study the diversified design based on the ship load analysis and capacity calculation model. An intelligent shore power device and system based on Internet of things technology is developed to solve the problems of short service life and high failure rate. Based on pneumatic and crawler lifting, slide rail positioning, air cushion block, electromagnetic sealing and automatic shielding device, tension sensing technology, control of automatic cable expansion and contraction technology. The intelligent shore power equipment and marine cable expansion device based on Internet of things control are developed, and the multi-dimensional information collection and monitoring such as charging data, operating parameters and environment temperature of the shore power equipment are developed. The utility model realizes the overall state perception, high-efficient information processing, convenient and flexible man-machine interaction application, and improves the operation stability of the shore power pile.

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“…Traditionally, this level of customization has been managed through the adaptation of Manufacturing resource planning systems (MRP-II or MRP), while the rise of Industry 4.0 is putting the emphasis on self-aware entities having increased autonomy for more responsive production control, paving the way for the transition towards more decentralized MPC system architectures [12][13][14][15]. This has brought to the development of hybrid MPC architectures whose aim is to break down, from a functional standpoint, the complex behavior of an MPC system to define a structure that is neither centralized (as the typical MRP is) nor decentralized [16][17][18][19]. In contrast, a heterarchical MPC structure entails a fully decentralized architecture, requiring the distribution of overall knowledge of the production system to each entity.…”

Section: Introductionmentioning

confidence: 99%

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

et al. 2023

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The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is still an open gap in the literature regarding advanced methodologies for production planning and control. Among different production and control approaches, hybrid architectures are gaining huge interest in the literature. For such architectures to operate at their best, reliable models for performance prediction of the supervised production system are required. In an effort to advance the development of hybrid architecture, this paper develops a model able to predict the performance of the controlled system when it is structured as a controlled work-in-progress (CONWIP) flow-shop with generalized stochastic processing times. To achieve this, we employed a simulation tool using both discrete-event and agent-based simulation techniques, which was then utilized to generate data for training a deep learning neural network. This network was proposed for estimating the throughput of a balanced system, together with a normalization method to generalize the approach. The results showed that the developed estimation tool outperforms the best-known approximated mathematical models while allowing one-shot training of the network. Finally, the paper develops preliminary insights about generalized performance estimation for unbalanced lines.

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Integration of industrial IoT architectures for dynamic scheduling

Cited by 11 publications

References 60 publications

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

An intelligent shore power device controlled by Internet of Things

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

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