Evaluating the resilience of the drug supply chain based on SWARA and CoCoSo multi criteria decision making approach with type 1 and interval type 2 fuzzy data (case study: Bojnord drug supply chain) - Fatemeh Bahman; Alireza Shahraki; Sayyid Ali Banihashemi (تصمیم گیری و تحقیق در عملیات)
Purpose: Based on the uncertainty in the supply chains, one of the important issues for public health is to assess the resilience of drug supply chains. The purpose of the current research is to determine and weigh the effective criteria for the resilience of the supply chain and the ranking of drug distribution companies based on the criteria.Methodology: The current research is quantitative-qualitative and applied. The statistical population was 83 people, and the sample was 68 people based on Morgan's table with a margin of error of 5%, which were selected by simple random sampling. Data collection was implemented with field methods, library studies, interviews and questionnaires. The questionnaire was approved by experts, and its reliability with Cronbach's alpha coefficient of 0.965. The percentage of personal characteristics was calculated using SPSS-26 software. Due to linguistic uncertainty, the weighting of the criteria was done with the fuzzy SWARA decision-making method and the ranking of the options for more accurate evaluation was performed by the interval type-2 fuzzy CoCoSo method in Excel software.Findings: The identified criteria in order of importance are knowledge management, agility, readiness and prediction, management method, supply chain design and structure, visibility and control, adaptability, collaboration, complementarity, innovation, complexity management, flexibility, uncertainty in the number of changes, and integration. The top three drug distribution companies are DarouPakhsh, AdoraTeb, and Ferdous, respectively.Originality/Value: The weighting of the criteria indicates which criteria have a greater impact on the resilience of the supply chain. Therefore, the company will be given a higher priority if it pays attention to criteria with higher weights.