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<p>Growing attention has recently been directed toward circular supply chains (CSCs) because of their significant contribution to mitigating environmental burdens. In response, this study addresses the design of a CSC while emphasizing two key dimensions: digitalization and sustainability. To this end, a data-driven framework is constructed through the integration of optimization models and machine learning (ML) techniques. First, a mathematical formulation is introduced to configure a digital and sustainable CSC. To manage mixed uncertainty, an ML-based mechanism is subsequently incorporated. The overall problem is then tackled using a hybrid solution strategy that combines Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) with metaheuristic algorithms. Computational evidence shows that this approach is capable of delivering optimal and near-optimal solutions within acceptable computational times. The findings further highlight the beneficial impact of Internet of Things (IoT) adoption on SC performance indicators such as revenues. In addition, results reveal that SC visibility improves substantially when a blockchain-enabled information sharing system (ISS) is implemented. Overall, the numerical analysis confirms the effectiveness of the proposed framework.</p>

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A Data-driven Approach to Design a Circular Supply Chain Network Based on Sustainability and Digitalization Dimensions Under Mixed Uncertainty

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Yasser Mohammadi Arbati,
  • Maryam Daneshmand-Mehr,
  • Hossein amoozad khalili,
  • Mirsaeid Hosseini Shirvani

摘要

Growing attention has recently been directed toward circular supply chains (CSCs) because of their significant contribution to mitigating environmental burdens. In response, this study addresses the design of a CSC while emphasizing two key dimensions: digitalization and sustainability. To this end, a data-driven framework is constructed through the integration of optimization models and machine learning (ML) techniques. First, a mathematical formulation is introduced to configure a digital and sustainable CSC. To manage mixed uncertainty, an ML-based mechanism is subsequently incorporated. The overall problem is then tackled using a hybrid solution strategy that combines Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) with metaheuristic algorithms. Computational evidence shows that this approach is capable of delivering optimal and near-optimal solutions within acceptable computational times. The findings further highlight the beneficial impact of Internet of Things (IoT) adoption on SC performance indicators such as revenues. In addition, results reveal that SC visibility improves substantially when a blockchain-enabled information sharing system (ISS) is implemented. Overall, the numerical analysis confirms the effectiveness of the proposed framework.