Deep Learning for Optimal Resource Allocation in IoT-enabled Additive Manufacturing

Mashhadi, Farshad; Monroy, Sergio A. Salinas

doi:10.1109/wf-iot48130.2020.9221038

Cited by 12 publications

(4 citation statements)

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“…Moreover, a type of ML technique inspired by the structure and functioning of the human brain, called NN, also received considerable attention. There were 11 articles, which are [31], [32], [34], [41], [56], [65], [69], [71], [73], [81], [87], found in this study. In the context of business plans and logistics decision processes, five articles address various challenges and research problems in improving production processes and decisionmaking using AI in Industry 4.0.…”

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confidence: 65%

Artificial intelligence and internet of things in manufacturing decision processes

Wijaya,

Hermanto Rudy,

Debora

et al. 2024

IJ-AI

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This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

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confidence: 65%

Artificial intelligence and internet of things in manufacturing decision processes

Wijaya,

Hermanto Rudy,

Debora

et al. 2024

IJ-AI

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“…The testing findings revealed that DIM's mean inventory demand forecast accuracy surpasses 80%, reducing inventory cost by 25% when compared to other state-of-the-art approaches and detecting anomalous inventory activities quickly. [36] presented that IoT-enabled manufacturing equipment could provide real-time data that https:// journal.uob.edu.bh could be used by the Additive Manufacturing (AM) cloud to automatically manage manufacturing resources. Extensive simulations confirmed that the suggested neural network auctions could find a better AM Cloud utility than existing auction schemes.…”

Section: Tianqing Et Al (2021)mentioning

confidence: 99%

Optimizing Resource Allocation in IoT for Improved Inventory Management

Kotru,

Batra

2024

IJCDS

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Effective inventory management is crucial for businesses to minimize costs and maximize operational efficiency. This paper explored the optimization of resource allocation on the Internet of Things (IoT) for improved inventory management and developed an inventory management system using IoT and Wireless Sensor Network (WSN) to optimize the resource allocation. In this paper, the dataset that is taken into consideration is the primary dataset, which is collected from different locations with the help of WSN, temperature, humidity, and stock of mapping of the place where data is allocated. Further, preprocessing of the data is done, and then the data is split as training and testing data. Machine learning models, i.e., decision tree, random forest, regression model, and ensemble model (combination of decision tree, random forest, and regression model), are applied to classify and train the data. The novelty of the research is establishing an inventory management system employing IoT and WSN, combining machine learning and ensemble models for resource allocation optimization, and outperforming traditional approaches. The result metrics such as Root Squared Mean Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Accuracy are taken into consideration to evaluate the performance of the model. Experimental results are obtained the values of RMSE, MAE, and MSE are 0.25, 0.0625, and 0.625, respectively. Also, the overall accuracy of the proposed model would be obtained as 93.75%. The comparative analysis shows that the proposed model outperformed the existing conventional model in terms of accuracy.

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“…However, it is clear that the allocation of orders in this distributed AM network is a major challenge for the implementation of this system, as different constraints and objectives (ranging from physical or technical features of the parts to price or transportation considerations) must be handled. Although the concept of cloud manufacturing is not specific of AM, there are several unique factors within the AM context, as in conventional manufacturing models it is often assumed that there are always resources to satisfy the demand, which is not always the case in AM (Mashhadi & Salinas Monroy, 2020), or that the customer (buyer) makes no distinction between the resources provided by different sources (Mashhadi & Salinas Monroy, 2019).…”

Section: Distributed Business Modelsmentioning

confidence: 99%

An overview on the use of operations research in additive manufacturing

Framiñán

Fernández-Viagas

2022

Ann Oper Res

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Additive Manufacturing (AM) is a disruptive production technology that challenges many well-established, conventional practices in Operations and Supply Chain Management decisions. Consequently, new context-specific decision problems have appeared in this area, while existing decision problems must be adapted or extended to this context. In this overview, these decision problems in AM are highlighted and classified, describing the different decision in an unified manner and citing the underlying OR techniques that have been applied to solve them. The aim of the paper is that, by presenting an overview of the AM-related problems in a systematic, OR- or Decision-Making-centred (rather than technology-centred) manner, the OR community can become more aware of this stream of research and thus be more active and contribute with some high-quality work. Open research challenges, as well as avenues for future research are also discussed.

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Deep Learning for Optimal Resource Allocation in IoT-enabled Additive Manufacturing

Cited by 12 publications

References 12 publications

Artificial intelligence and internet of things in manufacturing decision processes

Artificial intelligence and internet of things in manufacturing decision processes

Optimizing Resource Allocation in IoT for Improved Inventory Management

An overview on the use of operations research in additive manufacturing

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