A NOVEL DECISION-MAKING METHOD FOR RESILIENT SUPPLIER SELECTION DURING COVID-19 PANDEMIC OUTBREAK BASED ON HESITANT FUZZY LINGUISTIC PREFERENCE RELATIONS

Disasters such as the COVID-19 pandemic outbreak can bring challenges to trades all over the world. How to enhance the supply chain resilience and strengthen the competitiveness of enterprises in emergency situations has become a significant topic. To improve the emergency response capability of supply chains, this study aims at establishing a novel decision-making method to deal with the supplier selection problem during the COVID-19 pandemic outbreak based on a novel preference information represent tool, the hesitant fuzzy linguistic preference relation (HFLPR). To achieve this goal, we first introduce the cut of hesitant fuzzy linguistic elements and transform each hesitant fuzzy linguistic element in an HFLPR into the corresponding semantic considering the degree of confidence of the decision-makers regarding the evaluation values. Then, the utility matrix of an HFLPR is proposed. Afterwards, we construct two dual linear programming models to obtain interval weights from an HFLPR based on the concept of the least upper and the greatest lower bounds. An approach to ranking the obtained interval weights is introduced based on the concept of possibility degree between intervals. The procedure is summarised for the facility of implementation and we apply it to a case study about the resilient supplier selection during the COVID-19 pandemic outbreak. Some comparative analyses are presented to demonstrate the advantages of our method.