A new optimal consensus method with minimum cost in fuzzy group decision

Consensus models with aggregation operators for minimum quadratic cost in group decision making

In group decision making (GDM), to facilitate an acceptable consensus among the experts from different fields, time and resources are paid for persuading experts to modify their opinions. Thus, consensus costs are important for the GDM process. Notwithstanding, the unit costs in the common linear cost functions are always fixed, yet experts will generally express more resistance if they have to make more compromises. In this study, we use the quadratic cost functions, the marginal costs of which increase with the opinion changes. Aggregation operators are also considered to expand the applications of the consensus methods. Moreover, this paper further analyzes the minimum cost consensus models under the weighted average (WA) operator and the ordered weighted average (OWA) operators, respectively. Corresponding approaches are developed based on strictly convex quadratic programming and some desirable properties are also provided. Finally, some examples and comparative analyses are furnished to illustrate the validity of the proposed models.

An Evidential Prospect Theory Framework in Hesitant Fuzzy Multiple-Criteria Decision-Making

In numerous real decision-making problems, decision-makers (DMs) encounter situations involving hesitant and probabilistic information simultaneously, and DMs show behavior characteristics of nonrational preferences when they encounter decision-making situations with uncertain information. To address such multiple-criteria decision-making (MCDM) issues with hesitant and probabilistic information and nonrational preferences, a novel method, called the evidential prospect theory framework, is developed herein based on evidence theory and prospect theory, where the associated coefficients in prospect theory are given on the basis of symmetry principles (i.e., the associated coefficients are common knowledge to DMs). Within the proposed method, belief structures derived from evidence theory apply to the experts’ uncertainty about the subjective assessment of criteria for different alternatives. Then, by combining belief structures, the weighted average method is applied to estimate the final aggregated weighting factors of different alternatives. Furthermore, considering the nonrational preferences of DMs, the expected prospect values of different alternatives are derived from the final aggregated weighting factors and prospect theory, which is applied to the ranking order of all alternatives. Finally, a case involving a parabolic trough concentrating solar power plant (PTCSPP) is shown to illustrate the application of the novel method proposed in this paper. The evidential prospect theory framework proposed in this paper is effective and practicable, and can be applied to (green) supplier evaluation.

A ranking procedure by incomplete pairwise comparisons using information entropy and Dempster-Shafer evidence theory

Decision-making, as a way to discover the preference of ranking, has been used in various fields. However, owing to the uncertainty in group decision-making, how to rank alternatives by incomplete pairwise comparisons has become an open issue. In this paper, an improved method is proposed for ranking of alternatives by incomplete pairwise comparisons using Dempster-Shafer evidence theory and information entropy. Firstly, taking the probability assignment of the chosen preference into consideration, the comparison of alternatives to each group is addressed. Experiments verified that the information entropy of the data itself can determine the different weight of each group's choices objectively. Numerical examples in group decision-making environments are used to test the effectiveness of the proposed method. Moreover, the divergence of ranking mechanism is analyzed briefly in conclusion section.

A bio-inspired method for the constrained shortest path problem

The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method.

Bridge condition assessment using D numbers

Bridge condition assessment is a complex problem influenced by many factors. The uncertain environment increases more its complexity. Due to the uncertainty in the process of assessment, one of the key problems is the representation of assessment results. Though there exists many methods that can deal with uncertain information, however, they have more or less deficiencies. In this paper, a new representation of uncertain information, called D numbers, is presented. It extends the Dempster-Shafer theory. By using D numbers, a new method is developed for the bridge condition assessment. Compared to these existing methods, the proposed method is simpler and more effective. An illustrative case is given to show the effectiveness of the new method.