Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach

Pan, Wenjun; Miao, Lin

doi:10.1007/s11227-022-04727-6

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…While the probability of sudden emergencies is low, their occurrence can lead to catastrophic consequences, disrupting the flow of resources within the supply chain and affecting trade between nodes (Pan & Miao, 2023). The disruption caused by emergencies can lead to significant losses for individual enterprises and the entire supply chain system (Kim et al, 2023).…”

Section: Supply Chain Emergencies and Emergency Managementmentioning

confidence: 99%

Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

Gao

2024

International Journal of Information Systems and Supply Chain Management

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This study explores the integration of deep learning (DL) technology and the guided simulated annealing algorithm (GSAA) to optimize closed-loop supply chains (CLSC) for sustainable development. By applying DL for predictive analysis and GSAA for optimization, the research aims to enhance CLSC operational efficiency and environmental sustainability. The methodology combines a review of the CLSC framework with practical applications of DL and GSAA, aiming to reduce waste, maximize resource utilization, and minimize environmental impact. An experimental comparison of this approach against traditional optimization strategies demonstrates the proposed method's superior effectiveness and efficiency. The findings reveal that the DL-GSAA optimization significantly improves CLSC sustainability and efficiency, with GSAA showing promising convergence properties. This study underscores the importance of advanced technological solutions in achieving sustainable supply chain management, offering practical insights for businesses and supply chain managers.

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Section: Supply Chain Emergencies and Emergency Managementmentioning

confidence: 99%

Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

Gao

2024

International Journal of Information Systems and Supply Chain Management

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“…Their findings showed that the DL strategy based on the LSTM had the highest forecasting accuracy when compared to classical ML and time-series prediction methods. Using MATLAB, a SC risk evaluation model based on a BP neural network was created and assessed [13]. The outcomes of the simulation show that the suggested BP neural network model performs remarkably well in SC risk evaluation, with a maximum relative error of 0.03076923%.…”

Section: Deep Learning Techniques For Intelligent Supply Chain Risk P...mentioning

confidence: 99%

“…ML technique: In the logistics sector, [13] introduced a supervised ML approach to forecast SC risks. Nonetheless, the system employed a conventional feature representation approach, thereby warranting further exploration of deep neural networks that incorporate sophisticated embedding schemes to enhance efficacy.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

Alzahrani

Asghar

2023

Electronics

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The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that provides decision support to SC managers in order to achieve effective Internet of Things (IOT)-based logistics. Current research on predicting risks in shipping operations in the logistics sector during natural disasters has produced a variety of unexpected findings utilizing machine learning (ML) algorithms and traditional feature-encoding approaches. This has prompted a variety of concerns regarding the research’s validity. These previous attempts, like many others before them, used deep neural models to gain features without requiring the user to maintain track of all of the sequence information. This paper offers a hybrid deep learning (DL) approach, convolutional neural network (CNN) + bidirectional gating recurrent unit (BiGRU), to lessen the impact of natural disasters on shipping operations by addressing the question, “Can goods be shipped from a source location to a destination?”. The suggested DL methodology is divided into four stages: data collection, de-noising or pre-processing, feature extraction, and prediction. When compared to the baseline work, the proposed CNN + BiGRU achieved an accuracy of up to 94%.

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Impact pathways of emerging ITs to mitigate supply chain vulnerability: A novel DEMATEL-ISM approach based on grounded theory

Guo,

Yang,

Sun

et al. 2024

Expert Systems with Applications

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Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach

Cited by 7 publications

References 26 publications

Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

Impact pathways of emerging ITs to mitigate supply chain vulnerability: A novel DEMATEL-ISM approach based on grounded theory

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