Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making

• Published in: Annals of operations research 2023-06, Vol.325 (2), p.911-938
• Main Authors: Zhang, Zhen, Li, Zhuolin
• Format: Article
• Language: English
• Subjects: Algorithms; Alternatives; Analysis; Business and Management; Combinatorics; Context; Decision making; Feedback; Green buildings; Mathematical optimization; Multiple criteria decision making; Multiple criterion; Operations research; Operations Research/Decision Theory; Optimization models; Original Research; Sorting algorithms; Theory of Computation
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-022-04985-w

Due to the limited knowledge, experience and ability of a single expert, an increasing number of practical multi-criteria sorting (MCS) problems require the participation of multiple experts, which are called MCS problems in the context of group decision-making (MCS-GDM problems for short). To obtain consensual sorting results for alternatives, consensus reaching processes need to be considered in MCS-GDM problems. In this paper, two consensus-based TOPSIS-Sort-B algorithms are developed to deal with MCS-GDM problems. We first develop a minimum adjustment optimization model to obtain consensual boundary profiles by considering different experts’ boundary profiles. Based on individual decision matrices and the collective decision matrix, individual and group sorting results of alternatives can be obtained by using the TOPSIS-Sort-B method, respectively. Afterwards, different local adjustment strategy-based feedback adjustment mechanisms that can meet different needs are proposed to help experts adjust their assessments, and two consensus-based TOPSIS-Sort-B algorithms are designed to obtain consensual sorting results for MCS-GDM. Finally, a numerical example for green building rating and detailed simulation experiments are presented to justify the proposed algorithms and compare different feedback adjustment mechanisms.