A hybrid heterogeneous framework for medical waste disposal evaluation by fusing group BWM and regret-rejoice MABAC

• Published in: Expert systems with applications Vol. 249; p. 123514
• Main Authors: Chen, Ze-hui, Wu, Deng-feng, Luo, Wen, Cheng, Xian-juan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024
• Subjects: COVID-19; Emergency medical waste disposal; Group best-worst method; Heterogeneous information; Regret theory; COVID-19; Group best-worst method; Regret theory; Heterogeneous information; Emergency medical waste disposal
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2024.123514

•Propose a hybrid IVIF framework to evaluate the EMW disposal modes.•Develop an innovative IVIF-GBWM to derive the global criteria weights.•Fuse IVIF regret theory and MABAC to evaluate alternatives. This article attempts to develop a hybrid heterogeneous framework to evaluate the disposal modes for emergency medical waste (EMW) by fusing GBWM (group best-worst method), RT (regret theory), and MABAC (multi-attributive border approximation area comparison). This hybrid framework tries to address three key challenges: (i) determining criteria weights, (ii) computing the regret-rejoice evaluation matrix, and (iii) identifying the optimal alternative. To address the first challenge, this paper innovates an innovative interval-valued intuitionistic fuzzy (IVIF) GBWM, which considers multiplicatively consistent IVIF preference relation, to efficiently determine global criteria weights (GCWs). For the second challenge, RT is incorporated to compute the regret-rejoice evaluation matrix using experts' heterogeneous evaluations which consist of intervals, triangular fuzzy numbers, hesitant fuzzy linguistic term sets and IVIF values. Regarding the third challenge, MABAC is applied to sort alternatives based on the obtained regret-rejoice evaluation matrix. Afterwards, the validity of the hybrid framework is verified by an illustrative example, and its advantages are demonstrated with some worthy sensitivity and comparative analyses. Some innovations of the proposed framework are highlighted below: (i) The IVIF GBWM streamlines calculation load, since it derives GCWs by solving the group weight assignment model only once; (ii) The introduction of RT into MABAC assists decision-makers in mitigating regret psychology, enhancing the quality of decision outcomes; (iii) The proposed framework’s practicality is enhanced by leveraging heterogeneous evaluations which capture the inherent complexities of actual decision-making problems.