A robust decision framework for vertical farming using integrated objective weighting and distance driven ranking approach


Keerthana N. S., Narayanamoorthy S., Aarthi K., Geetha S., Almakayeel N., PAMUCAR D., ...daha çox

Scientific Reports, vol.15, no.1, 2025 (SCI-Expanded, Scopus) identifier identifier identifier

  • Nəşrin Növü: Article / Article
  • Cild: 15 Say: 1
  • Nəşr tarixi: 2025
  • Doi nömrəsi: 10.1038/s41598-025-24792-0
  • jurnalın adı: Scientific Reports
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
  • Açar sözlər: Logarithmic percentage change-driven objective weighting, Multi-attribute border approximation area comparison, Multi-Criteria Decision Making, q-rung picture fuzzy set, Vertical farming techniques
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Bəli

Qısa məlumat

Vertical Farming Techniques cultivates plants without soil, leading to greater productivity and profit. This unconventional farming technique requires minimal resources while producing high-quality yields. This study offers a comprehensive analysis of these methods, including hydroponics, aquaponics, aeroponics and fogponics. The quantification of uncertainty and ambiguity is done by employing the q-rung picture fuzzy set. This study introduces a novel hybrid Logarithmic percentage change-driven objective weighting- Multi-attributive border approximation area comparison framework, which provides objective analysis based on the distance measures. This study has concluded that the production yield plays a vital role in the selection of the optimal technology. The final results conclude that aquaponics is the ideal vertical farming method, which is followed by aeroponics, fogponics and hydroponics. The results of the proposed framework are validated and the reliability of the methodology is established by conducting a comparison analysis. Furthermore, Spearman’s rank correlation has been conducted for better understanding of the results of the comparison analysis. Finally, sensitivity analysis is performed to establish the model’s accuracy.