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<p>Nowadays, researchers have a significant difficulty in managing low-carbon supply chains due to rising global temperatures and concerns about the environment. As concerns about the environment and social economy trends develop, businesses must focus on low-carbon supply chain management to attain profitable growth. Because the root of the chain is dependent on selecting a high-quality low-carbon supply. To achieve low-carbon supply chain management (LCGSCM), it is important to establish strict rules and a process for choosing for green suppliers. A systematic approach is needed to solve these issues, one that involves making technological investments, encouraging supplier collaboration, keeping up with regulatory changes, and consistently enhancing measuring procedures. The long-term advantages of a low-carbon, green supply chain—such as increased sustainability, regulatory compliance, and improved brand reputation—often exceed the problems, despite their sizeable nature. The LCGSCM has emerged as a critical area of concentration for businesses seeking to reduce their environmental footprint while preserving economic viability in the fight for sustainability. This work uses N-cubic fuzzy aggregation operators in conjunction with multi-attribute decision-making (MADM) techniques to provide a novel solution for LCGSCM. The N-cubic fuzzy model offers a strong framework for managing ambiguity and uncertainty in decision-making, allowing for the more accurate processing of both positive and negative assessments. We create a framework for decision-making that optimizes resource allocation, operational strategies, and supplier selection in a low-carbon environment by integrating these sophisticated operators. Through real-world case studies, the suggested model’s efficacy in attaining sustainable supply chain management in challenging, unpredictable circumstances is revealed. For this, we propose some operators as stated in arithmetic as well as geometric operations to handle N-Cubic fuzzy sets (NCFSs) in DM, that are NCF weighted average (NCFWA), NCF weighted geometric (NCFWG), NCF order weighted (NCFOW), NCF order weighted geometric (NCFOWG), NCF hybrid (NCFH), and NCF hybrid geometric (NCFHG) operators. Following that, we will discuss how they can be applied in dealing with NCF multiple attribute decision-making (MADM) problems. We will also explore an actual problem related to sustainable LCGSCM to test the viability and flexibility of the suggested technique.</p>

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Multi-attribute Decision-Making Methods to Low-Carbon Green Supply Chain Management in N-Cubic Fuzzy Aggregation Operators

  • 期刊论文

  • 发表日期：

    2025年9月21日

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  • Sheikh Rashid,
  • Muhammad Gulistan,
  • Tahir Abbas,
  • Kholood Alsager,
  • Muhammad Rahim

摘要

Nowadays, researchers have a significant difficulty in managing low-carbon supply chains due to rising global temperatures and concerns about the environment. As concerns about the environment and social economy trends develop, businesses must focus on low-carbon supply chain management to attain profitable growth. Because the root of the chain is dependent on selecting a high-quality low-carbon supply. To achieve low-carbon supply chain management (LCGSCM), it is important to establish strict rules and a process for choosing for green suppliers. A systematic approach is needed to solve these issues, one that involves making technological investments, encouraging supplier collaboration, keeping up with regulatory changes, and consistently enhancing measuring procedures. The long-term advantages of a low-carbon, green supply chain—such as increased sustainability, regulatory compliance, and improved brand reputation—often exceed the problems, despite their sizeable nature. The LCGSCM has emerged as a critical area of concentration for businesses seeking to reduce their environmental footprint while preserving economic viability in the fight for sustainability. This work uses N-cubic fuzzy aggregation operators in conjunction with multi-attribute decision-making (MADM) techniques to provide a novel solution for LCGSCM. The N-cubic fuzzy model offers a strong framework for managing ambiguity and uncertainty in decision-making, allowing for the more accurate processing of both positive and negative assessments. We create a framework for decision-making that optimizes resource allocation, operational strategies, and supplier selection in a low-carbon environment by integrating these sophisticated operators. Through real-world case studies, the suggested model’s efficacy in attaining sustainable supply chain management in challenging, unpredictable circumstances is revealed. For this, we propose some operators as stated in arithmetic as well as geometric operations to handle N-Cubic fuzzy sets (NCFSs) in DM, that are NCF weighted average (NCFWA), NCF weighted geometric (NCFWG), NCF order weighted (NCFOW), NCF order weighted geometric (NCFOWG), NCF hybrid (NCFH), and NCF hybrid geometric (NCFHG) operators. Following that, we will discuss how they can be applied in dealing with NCF multiple attribute decision-making (MADM) problems. We will also explore an actual problem related to sustainable LCGSCM to test the viability and flexibility of the suggested technique.