A hybrid decision-support framework for selecting sustainable domestic heating systems in cold climates


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Suvitha K., Murugesan V., Jaisankar R., Narayanamoorthy S., Almakayeel N., DİNÇER H., ...More

Scientific Reports, vol.16, no.1, 2026 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1038/s41598-026-47813-y
  • Journal Name: Scientific Reports
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
  • Keywords: AHP–EDAS, Domestic heating selection, Energy efficiency, Hybrid MCDM, Probabilistic hesitant fuzzy sets, Sustainable heating systems
  • Open Archive Collection: Digital Heritage Collection
  • Azerbaijan State University of Economics (UNEC) Affiliated: No

Abstract

This study proposes a probabilistic hesitant fuzzy multi-criteria decision-making approach for assessing sustainable heating system alternatives under uncertain situations. The selection of heating systems is a major issue in sustainability in cold climate regions due to its high energy requirements. The proposed approach integrates the Probabilistic Hesitant Fuzzy Analytic Hierarchy Process (PHF-AHP) for determining the weights of decision criteria and the Evaluation Based on Distance from Average Solution (EDAS) for ranking alternatives. The probabilistic hesitant fuzzy approach allows decision-makers to reflect uncertainty and hesitation in decision-making under expert judgments by considering various membership values with their respective probabilities. To validate the proposed methodological framework, a case study of five heating system alternatives is presented. The results reveal that the proposed PHF-AHP-EDAS approach is reliable in ranking alternatives under ambiguity and uncertainty in sustainability decision problems.