This paper proposes a novel consensus reaching process (CRP) for the two-rank group decision-making (GDM) problems with heterogeneous preference information. The methods for deriving the individual and collective preference vector are provided. And the individual and collective two-rank vectors are obtained. Then, the feedback adjustment rules are proposed. Next, an algorithm is given to describe the two-rank CRP with heterogeneous preference information. Finally, we present a practical example to illustrate the feasibility of the proposed method.

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field.

Multiple attribute decision making (MADM) is used to rank the alternatives according to evaluation information based on multiple attributes, and many MADM methods have been studied to deal with the MADM problems. In existing MADM methods, when setting different attribute weights, the ranking of alternatives are different.And ranking range can be used to measure a lower bound and an upper bound of rankings of alternatives with the change of the attribute weights. Also, in some real MADM problems, the information on attribute weights may be unknown or partially known, which is called incomplete attribute weight information. Then, this study investigates the ranking range models (RRMs) under incomplete attribute weight information in the selected six MADM methods: Weighted geometric averaging (WGA), Ordered weighted geometric averaging (OWGA), TOPSIS, VIKOR, PROMETHEE and ELECTRE. Particularly, we can construct several 0-1 mathematical programming models to compute the ranking range of alternatives under incomplete attribute weight information for the selected six MADM methods. Then, two case studies on project investment and Academic Ranking of World Universities (ARWU) are used to justify the validity of the RRMs under incomplete attribute weight information in the selected six MADM methods. K E Y W O R D S incomplete attribute weight information, mixed 0-1 mathematical programming models, multiple attribute decision making, ranking range 1 | INTRODUCTION Multiple attribute decision making (MADM) is devoted to the problem where a limited number of alternatives are ranked based on evaluation information on multiple attributes (

Linguistic preference relations are widely used by decision makers to elicit their preferences over alternatives in the Group Decision Making (GDM) process. Recent studies have shown that self-confidence, as an important human psychological behavior, has an important influence on decision-making results. However, multiple self-confidence levels of decision makers are seldom considered in the linguistic preference relation. Meanwhile many real-word decision-making problems are analyzed in a hierarchical structure, in which a complicated problem can be divided into several easier comprehended sub-problems. Hence, this paper aims at designing a linguistic hierarchy model with self-confidence preference relation (LHM-SCPR) to discuss complex GDM problems in a hierarchical structure. In the SC-LPR, each element contains two components, the first one is the preference value between pairs of alternatives, and the second one that is defined on a linguistic term set represents decision maker’s self-confidence level associated to the first component. Meanwhile, a nonlinear programming model is proposed to derive individual preference vector from SC-LPR. Then, we apply LHM-SCPR in co-regulation of food safety to present the validity of this method, and find that improving the participation skills regarding co-regulation of food safety is the most pressing task. Finally, detailed comparative analysis and discussion are presented to verify the validity of the proposal.