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<p>The goal of the operational research field of “multiple criteria decision-making (MCDM)” is to determine the best course of action in situations when there are numerous competing goals, competing criteria, and a large number of indicators. For routine decision-making in a range of businesses, MCDM approaches are a great tool. Furthermore, it is a challenging and complex task to come up with an appropriate response in light of multiple circumstances. This research work introduced a new technique for solving the MCDM problem under hesitant fuzzy sets (HFSs) called FSCT (Fuzzy Stable Compromise Technique), which is based on <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12190_2025_2538_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="38" /> </InlineMediaObject> <EquationSource Format="TEX">\(\left( {p,q} \right){\text{ }}\)</EquationSource> </InlineEquation>relative metrics. The membership degree of an HFS is made up of several possible numbers. HFS is a powerful tool for effectively handling imprecise and uncertain information in decision-making. We demonstrate the usefulness of our method in MCDM scenarios using hesitant fuzzy preference data and criterion weight with a real-world case. We suggested this novelty, which we call FSCT. As per the FSCT, the most optimal options, according to hesitant fuzzy information, are the best car alternative <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12190_2025_2538_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="20" /> </InlineMediaObject> <EquationSource Format="TEX">\({A_4}\)</EquationSource> </InlineEquation> and in a real-case study, Stanford University’s alternative <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12190_2025_2538_Article_IEq3.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="20" /> </InlineMediaObject> <EquationSource Format="TEX">\({A_6}{\text{ }}\)</EquationSource> </InlineEquation> respectively.</p>

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The development of a fuzzy stable compromise technique (FSCT) under hesitant fuzzy sets

  • 期刊论文

  • 发表日期：

    2025年6月11日

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  • Rafi Raza,
  • Ahmad Termimi Ab Ghani,
  • Lazim Abdullah

摘要

The goal of the operational research field of “multiple criteria decision-making (MCDM)” is to determine the best course of action in situations when there are numerous competing goals, competing criteria, and a large number of indicators. For routine decision-making in a range of businesses, MCDM approaches are a great tool. Furthermore, it is a challenging and complex task to come up with an appropriate response in light of multiple circumstances. This research work introduced a new technique for solving the MCDM problem under hesitant fuzzy sets (HFSs) called FSCT (Fuzzy Stable Compromise Technique), which is based on \(\left( {p,q} \right){\text{ }}\) relative metrics. The membership degree of an HFS is made up of several possible numbers. HFS is a powerful tool for effectively handling imprecise and uncertain information in decision-making. We demonstrate the usefulness of our method in MCDM scenarios using hesitant fuzzy preference data and criterion weight with a real-world case. We suggested this novelty, which we call FSCT. As per the FSCT, the most optimal options, according to hesitant fuzzy information, are the best car alternative \({A_4}\) and in a real-case study, Stanford University’s alternative \({A_6}{\text{ }}\) respectively.