OPTIMAL CONSENSUS RANKING USING SLS: AN APPROACH AND AN APPLICATION (original) (raw)

Model of Decision Making through Consensus in Ranking Case

IOP Conference Series: Materials Science and Engineering, 2018

The basic problem to determine ranking consensus is a problem to combine some rankings those are decided by two or more Decision Maker (DM) into ranking consensus. DM is frequently asked to present their preferences over a group of objects in terms of ranks, for example to determine a new project, new product, a candidate in a election, and so on. The problem in ranking can be classified into two major categories; namely, cardinal and ordinal rankings. The objective of the study is to obtin the ranking consensus by appying some algorithms and methods. The algorithms and methods used in this study were partial algorithm, optimal ranking consensus, BAK (Borde-Kendal)Model. A method proposed as an alternative in ranking conssensus is a Weighted Distance Forward-Backward (WDFB) method, which gave a little difference i ranking consensus result compare to the result oethe example solved by Cook, et.al (2005).

Managing Consensus by Multi-Stage Optimization Models with Linguistic Preference Orderings and Double Hierarchy Linguistic Preferences

Technological and Economic Development of Economy, 2020

Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and ...

Analysis of the Final Ranking Decisions Made by Experts After a Consensus has Been Reached in Group Decision Making

Group Decision and Negotiation, 2020

Traditional approaches to group decision making (GDM) problems for ranking a finite set of alternatives terminate when the experts involved in the GDM process reach a consensus. This paper proposes ways for analyzing the final results after a consensus has been reached in GDM. Results derived from this last step can be used to further enhance the understanding of possible hidden dynamics of the problem under consideration. The proposed approach for post-consensus analysis is in part based on a novel idea, known as preference maps (PMs) introduced recently in the literature on how rankings should be described when ties in the rankings are allowed. An original contribution of this paper is how to define the difference between two PMs. This is achieved by using a metric known as the Marczewski-Steinhaus distance. Approaches for analyzing the final results of a GDM process after consensus has been reached may reveal hidden but crucial insights in the way the experts reached the consensus and also new insights related to the alternatives. These approaches rely on the concept of differences in the rankings, defined by traditional means or as the difference between two PMs as defined in this paper. This is the second group of original contributions made in this paper. The various issues are illustrated with numerical examples and an application inspired from a real-world problem described in the literature. The new contributions described in this study offer an exciting potential to enrich the group decision making process considerably.

A consensus model for multiperson decision making with different preference structures

2002

Abstract In this paper, we present a consensus model for multiperson decision making (MPDM) problems with different preference structures based on two consensus criteria: 1) a consensus measure which indicates the agreement between experts' opinions and 2) a measure of proximity to find out how far the individual opinions are from the group opinion. These measures are calculated by comparing the positions of the alternatives between the individual solutions and collective solution.

On Consensus in Group Decision Making Based on Fuzzy Preference Relations

Studies in Fuzziness and Soft Computing, 2011

In the process of decision making, the decision makers usually provide inconsistent fuzzy preference relations, and it is unreasonable to get the priority from an inconsistent preference relation. In this paper, we propose a method to derive the multiplicative consistent fuzzy preference relation from an inconsistent fuzzy preference relation. The fundamental characteristic of the method is that it can get a consistent fuzzy preference relation considering all the original preference values without translation. Then, we develop an algorithm to repair a fuzzy preference relation into the one with weak transitivity by using the original fuzzy preference relation and the constructed consistent one. After that, we propose an algorithm to help the decision makers reach an acceptable consensus in group decision making. It is worth pointing out that group fuzzy preference relation derived by using our method is also multiplicative consistent if all individual fuzzy preference relations are multiplicative consistent. Some examples are also given to illustrate our results.

Choosing and Ranking on the Basis of Fuzzy Preference Relations with the “Min in Favor”

Lecture Notes in Economics and Mathematical Systems, 1997

In some MCDM techniques -most notably in Outranking Methodsthe result of the comparison of a finite set of alternatives according to several criteria is summarized using a fuzzy preference relation. This fuzzy relation does not, in general, possess "nice properties" such as transitivity or completeness and elaborating a recommendation on the basis of such information is not an obvious task. The purpose of this paper is to study techniques exploiting fuzzy preference relations in order to choose or rank. We present a number of results concerning techniques based on the "min in Favor" score, i.e. the minimum level with which an alternative is "at least as good as" all other alternatives.

A Consensus Support System Model for Group Decision-Making Problems With Multigranular Linguistic Preference Relations

IEEE Transactions on Fuzzy Systems, 2005

The group decision-making framework with linguistic preference relations is studied. In this context, we assume that there exist several experts who may have different background and knowledge to solve a particular problem and, therefore, different linguistic term sets (multigranular linguistic information) could be used to express their opinions. The aim of this paper is to present a model of consensus support system to assist the experts in all phases of the consensus reaching process of group decision-making problems with multigranular linguistic preference relations. This consensus support system model is based on i) a multigranular linguistic methodology, ii) two consensus criteria, consensus degrees and proximity measures, and iii) a guidance advice system. The multigranular linguistic methodology permits the unification of the different linguistic domains to facilitate the calculus of consensus degrees and proximity measures on the basis of experts' opinions. The consensus degrees assess the agreement amongst all the experts' opinions, while the proximity measures are used to find out how far the individual opinions are from the group opinion. The guidance advice system integrated in the consensus support system model acts as a feedback mechanism, and it is based on a set of advice rules to help the experts change their opinions and to find out which direction that change should follow in order to obtain the highest degree of consensus possible. There are two main advantages provided by this model of consensus support system. Firstly, its ability to cope with group decision-making problems with multigranular linguistic preference relations, and, secondly, the figure of the moderator, traditionally presents in the consensus reaching process, is replaced by the guidance advice system, and in such a way, the whole group decision-making process is automated.