Two-sided matching optimization model for green housing technology selection based on hesitant 2-tuple linguistic rough numbers

Abstract

In two-sided matching decision problems, the matching objects with different knowledge, experiences, and cultures provide linguistic assessments using diverse or multi-granular sets with a factor that the information provided is hesitant in nature due to different opinions given by experts. In the proposed approach, the hesitant 2-tuple linguistic information is integrated with rough approximations to develop the two novel approaches called hesitant rough numbers and hesitant 2-tuple linguistic rough numbers. The proposed novel approximations are implemented on a two-sided matching optimization model to study hesitant multi-granular uncertainty. Firstly, the matching objects provide their evaluations in the form of hesitant multi-granular terms converted into hesitant 2-tuple linguistic rough numbers. Secondly, certain optimization models based on hesitant 2-tuple linguistic rough approximations are constructed to compute the criteria weights using incomplete information. In hesitant 2-tuple linguistic rough optimization models, the maximizing deviation technique is used to find the distance between two proposed novel coefficients. To maximize the level of satisfaction with matching objects, a hesitant 2-tuple linguistic rough optimization model is developed to evaluate the overall satisfaction degree and stability of matching objects. The significance of the proposed two-sided matching optimization model is illustrated with a case study of matching between the green building technology supply and demand. The out-performance of the proposed model is highlighted by a comparison analysis with existing approaches to analyze that it can provide hesitant multi-granular rough flexibility and deals with incomplete information regarding criterion weights.