Scholarlink

<p>In recent years, increasing environmental concerns have attracted the attention of researchers and managers toward configuring circular supply chains. In this regard, this article attempts to configure a circular supply chain (SC) by considering two crucial dimensions namely digitalization and resilience. For this purpose, this study proposes a novel hybrid data-driven optimization framework for configuring a digital and resilient circular supply chain under mixed uncertainty. The model integrates resilience metrics, Industry 4.0 capabilities (IoT-based monitoring and blockchain-enabled information sharing), and circular economy principles into a single multi-objective mathematical formulation. The hybrid uncertainty is modeled by combining the Fuzzy Robust Stochastic (FRS) method with the Prophet forecasting algorithm. To solve the NP-hard model efficiently, the study applies the Fuzzy Lexicographic Multi-Choice Achievement Chebyshev Goal Programming (FLMCACGP) method in combination with a heuristic approach. A real-world case study in the mobile phone industry demonstrates the model’s capability to produce optimal or near-optimal solutions with practical managerial insights. The proposed model uniquely couple’s resilience metrics with Industry 4.0 capabilities such as IoT-based product monitoring and blockchain-based visibility enhancement. The obtained results confirm the effectiveness of the developed heuristic-based method because it can obtain optimal/near-optimal solutions in a reasonable time. Also, the achieved outputs demonstrate the positive role of the Internet of Things (IoT) in revenues and costs. On the other hand, the results show that using a blockchain-based information-sharing system can dramatically improve the visibility of the SC. Finally, managerial insights have been presented.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A machine learning-based model to configure a resilient circular supply chain network based on the industry 4.0 dimensions: a case study

  • 期刊论文

  • 发表日期：

    2025年12月23日

查看全文
  • Mahsa Ebrahim Poorsabet,
  • Ali Shahabi

摘要

In recent years, increasing environmental concerns have attracted the attention of researchers and managers toward configuring circular supply chains. In this regard, this article attempts to configure a circular supply chain (SC) by considering two crucial dimensions namely digitalization and resilience. For this purpose, this study proposes a novel hybrid data-driven optimization framework for configuring a digital and resilient circular supply chain under mixed uncertainty. The model integrates resilience metrics, Industry 4.0 capabilities (IoT-based monitoring and blockchain-enabled information sharing), and circular economy principles into a single multi-objective mathematical formulation. The hybrid uncertainty is modeled by combining the Fuzzy Robust Stochastic (FRS) method with the Prophet forecasting algorithm. To solve the NP-hard model efficiently, the study applies the Fuzzy Lexicographic Multi-Choice Achievement Chebyshev Goal Programming (FLMCACGP) method in combination with a heuristic approach. A real-world case study in the mobile phone industry demonstrates the model’s capability to produce optimal or near-optimal solutions with practical managerial insights. The proposed model uniquely couple’s resilience metrics with Industry 4.0 capabilities such as IoT-based product monitoring and blockchain-based visibility enhancement. The obtained results confirm the effectiveness of the developed heuristic-based method because it can obtain optimal/near-optimal solutions in a reasonable time. Also, the achieved outputs demonstrate the positive role of the Internet of Things (IoT) in revenues and costs. On the other hand, the results show that using a blockchain-based information-sharing system can dramatically improve the visibility of the SC. Finally, managerial insights have been presented.