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<p>This article analyzes the viability and potential consequences of deploying electric cars (EVs) in developing countries. To tackle the difficulties of hesitant, fuzzy, and linguistically complicated assessments in multi-criteria decision-making (MCDM), this study proposes a unique framework that uses Linguistic Fermatean Hesitant Fuzzy Sets (LFHFSs). Advanced aggregation operators, such as LFHF weighted arithmetic and geometric operators and new distance measures, are incorporated into the suggested LFHFS architecture. The study used these tools to increase the resilience and precision of decision-making by refining the Evaluation based on Distance from Average Solution (EDAS) technique. Compared to current methods, the improved LFHFS-EDAS methodology offers superior validity and distinguishability while effectively handling complicated and ambiguous data. The weight allocation problem may be resolved using the LFHFS-EDAS technique when there are many indices. The effectiveness of the suggested method in choosing the best plans for starting EV industries is illustrated through a case study, highlighting its ability to solve actual MCDM issues.</p>

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Multi-criteria decision-making with EDAS method and weighted operators for linguistic Fermatean hesitant fuzzy sets

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Muhammad Amman,
  • Tabasam Rashid,
  • Asif Ali

摘要

This article analyzes the viability and potential consequences of deploying electric cars (EVs) in developing countries. To tackle the difficulties of hesitant, fuzzy, and linguistically complicated assessments in multi-criteria decision-making (MCDM), this study proposes a unique framework that uses Linguistic Fermatean Hesitant Fuzzy Sets (LFHFSs). Advanced aggregation operators, such as LFHF weighted arithmetic and geometric operators and new distance measures, are incorporated into the suggested LFHFS architecture. The study used these tools to increase the resilience and precision of decision-making by refining the Evaluation based on Distance from Average Solution (EDAS) technique. Compared to current methods, the improved LFHFS-EDAS methodology offers superior validity and distinguishability while effectively handling complicated and ambiguous data. The weight allocation problem may be resolved using the LFHFS-EDAS technique when there are many indices. The effectiveness of the suggested method in choosing the best plans for starting EV industries is illustrated through a case study, highlighting its ability to solve actual MCDM issues.