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Sorting radiology departments in a disaster management assessment with G-ARASsort

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  • Arash Moheimani
  • Alessio Ishizaka
  • Seyed Mohammad Hassan Hosseini
  • Sachin Kumar Mangla

Abstract

This study presents a new multiple criteria group decision making (MCGDM) sorting method, named G-ARASsort – a new variant of the ARAS ranking method, to address a disaster management assessment problem through a group consensus. MCGDM sorting methods improve a decision-making problem by providing useful and unparalleled insights. Disaster management is one of the most critical areas of decision-making, where decisions are made under tremendous pressure. Radiology units play a central role in disaster management and the present study establishes a new disaster management assessment framework to assess their disaster readiness. The proposed method is applied to sort 37 radiology units into four ordered classes, defined through a Delphi panel. The results show that approximately 11% and 14% of the departments placed in the first (best) and the last (worst) classes, respectively. The findings can help the lower-class units improve their performance by using the upper-class units’ performance as a yardstick. Additionally, healthcare directors can identify more reliable radiology units to turn to during disasters and prevent patient misallocation. A comparative analysis is also conducted to confirm the method’s competency.

Suggested Citation

  • Arash Moheimani & Alessio Ishizaka & Seyed Mohammad Hassan Hosseini & Sachin Kumar Mangla, 2025. "Sorting radiology departments in a disaster management assessment with G-ARASsort," International Journal of Production Research, Taylor & Francis Journals, vol. 63(2), pages 459-482, January.
  • Handle: RePEc:taf:tprsxx:v:63:y:2025:i:2:p:459-482
    DOI: 10.1080/00207543.2023.2232477
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