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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Resource Management and Decision Engineering</JournalTitle>
      <Issn></Issn>
      <Volume>5</Volume>
      <Issue>Serial Number 16</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>05</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Designing an Intelligent Sustainable Supply Chain Model in the Gas Industry Using Industry 4.0 Technologies under Conditions of Uncertainty</ArticleTitle>
    <VernacularTitle>Designing an Intelligent Sustainable Supply Chain Model in the Gas Industry Using Industry 4.0 Technologies under Conditions of Uncertainty</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>24</LastPage>
    <ELocationID EIdType="doi">10.61838/kman.jrmde.4</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2025</Year>
        <Month>08</Month>
        <Day>04</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;With the intensification of environmental pressures, volatility in energy markets, and increasing complexity in operational environments, gas industry supply chains are confronted with high levels of uncertainty and multidimensional risks. In this context, the simultaneous adoption of sustainability-oriented approaches and Industry 4.0 technologies can be considered a key strategy for enhancing efficiency, resilience, and competitiveness across the supply chain. The purpose of this study is to design and conceptualize an intelligent sustainable supply chain model for the gas industry, with an emphasis on Industry 4.0 technologies under conditions of uncertainty. The present research is applied in nature and adopts a mixed-methods (qualitative–quantitative) methodological approach. In the first stage, a systematic literature review was conducted using the PRISMA protocol to identify the initial dimensions and indicators. Subsequently, a fuzzy Delphi method was employed to validate and refine the indicators through the participation of academic and industry experts. In the next step, the weighting of criteria and sub-criteria under uncertainty was performed using the fuzzy Best–Worst Method. To reduce model complexity and focus on key factors, hierarchical clustering was applied, and the selected criteria were entered into the causal relationship analysis phase. At this stage, a fuzzy cognitive map was utilized to analyze the degree of influence and dependence among factors and to identify the system’s driving variables. The results indicate that factors such as commitment to environmental sustainability, investment in intelligent technologies, and the strategic alignment of sustainability with Industry 4.0 exhibit the highest levels of centrality and driving power in enhancing the sustainable performance of the gas supply chain. The findings provide a systematic and decision-support framework for managers in the gas industry and can serve as a basis for policymaking and strategic planning aimed at developing intelligent and sustainable supply chains under conditions of uncertainty.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Sustainable supply chain</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Industry 4</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Uncertainty</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Fuzzy multi-criteria decision-making</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Fuzzy cognitive map</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journalrmde.com/index.php/jrmde/article/download/242/360</ArchiveCopySource>
  </Article>
</ArticleSet>
