Optimizing Robot Deployment in Hazardous Environment: MCDM Approach Using Field Performers Under Intuitionistic Dense Fuzzy Set

The lives of humans working in hazardous fields are often at risk. Therefore, replacing humans with robots can help to reduce the mortality rate. Hence, this work considers four dangerous operations, namely, earthquake rescue, fire rescue, water rescue and bomb disposal operation, and determines the most wanted field for the replacement of robots concerning their death rate. The fields’ prioritization is examined using multi-criteria decision-making (MCDM) models. Since the alternatives (fields) lack information with respect to the criteria, this work utilizes the sub-alternatives (field performer) to fill in the information of the alternatives in order to prioritize the fields and detect the impact of the robots in the field. When uncertainty prevails in crisp data, the MCDM methods are generally considered in a fuzzy environment. The intuitionistic trapezoidal dense fuzzy (ITpDF) set which is one of the extensions of fuzzy set is dealt here. Considering the MCDM models such as extended PSI, COCOSO, PIV and MARCOS in the ITpDF field, the integrated ITpDF–PSI–COCOSO–PIV–MARCOS model is framed, where the final ITpDF score values from each considered models are aggregated using the Bonferroni mean aggregation method. The aggregated ITpDF score values are defuzzified and ranked using the novel ITpDF incircle ranking. The result reveals that earthquake rescue robots have a lesser impact compared to robots utilized in bomb disposal operations. Finally, the sensitivity and comparative analysis are performed to find the effectiveness of the model and the quality of the outcome, respectively.