Search

Consensus reaching process mainly includes consensus measurement and consensus improvement. Ignoring the bounded rational behavior of group members will affect the credibility of decision results. This paper proposes a consensus model that considers bounded rational behaviors of group members under uncertain environment. Firstly, we propose a similarity measure of probabilistic linguistic term sets (PLTSs) based on the Jensen-Shannon divergence, which can measure the difference between different probability distributions corresponding to PLTSs that are used to represent uncertain information. Then, the DEMATEL method is used to determine the weights of criteria according to the relationship between criteria, and a weight penalty mechanism is introduced to improve the consensus level of a group. Next, we combine the TODIM and VIKOR methods to elicit the ranking of alternatives. Finally, a numerical example is given to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set is a flexible and efficient tool to represent the cognitive complex information of experts. It has attracted many scholars' attention since it was proposed. Information fusion over the cognitive complex information is a significant issue for decision‐making problems. Over the past years, more than 40 aggregation operators have been proposed to fuse the probabilistic linguistic term sets. The aim of this paper is to survey the existing probabilistic linguistic aggregation operators from the perspectives of principles, definitions, classifications, and applications. To do so, first, we summarize the present normalization techniques and operations of probabilistic linguistic term sets. Afterward, this study classifies the existing probabilistic linguistic aggregation operators into 12 kinds. Then, the application areas of these probabilistic linguistic aggregation operators are outlined. Future research directions with interests are proposed to tackle present challenges. [ABSTRACT FROM AUTHOR]

Multiple-criteria decision-making is common in our daily life. The probabilistic linguistic term set is an effective tool to represent both simple and cognitive complex linguistic expressions given by individuals and groups completely. In this paper, the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) is enhanced by integrating with a conflict analysis to solve general multiple-criteria decision-making problems with both quantitative and qualitative criteria. Firstly, to capture the inherent uncertainty of evaluations, interval numbers are used to expresses the values of quantitative criteria while probability linguistic term sets are used to scale the qualitative criteria. Then, a preference function for both quantitative and qualitative criteria is proposed. In addition, a conflict analysis is presented and added to the PROMETHEE, which can derive the preference, indifference, and incomparability (PIR) relations of alternatives. A reference point is given to select the thresholds for the PIR relations. Finally, the improved PROMETHEE is highlighted by a case study concerning site selection of the wind power plant. [ABSTRACT FROM AUTHOR]

The quality function deployment (QFD) is an effective tool to translate the customer requirements (CRs) to the design requirements (DRs) of a product. The process of selecting the optimal innovative product design to maximize customer satisfaction is full of uncertainty and fuzziness regarding to the users’ preferences, the relationships between CRs and DRs and the merits of product designs. This study proposes a multi-expert multi-criteria decision making method to solve the innovative product design selection problem by developing an enhanced QFD method combined with the complicated fuzzy linguistic representation model, the probabilistic linguistic term set (PLTS), and the ranking method, ORESTE. Firstly, we propose a probability aggregation method to integrate the individuals’ subjective evaluations into group ones expressed as PLTSs. On this basis, we extend the QFD into the probabilistic linguistic context to get the DRs’ fuzzy weights. Then, based on a new distance measure between PLTSs, a probabilistic linguistic global preference score function and three kinds of probabilistic linguistic preference intensity formulas are proposed. Furthermore, we develop a PL-ORESTE method to obtain the preference, indifference and incomparability relations between the alternatives. For the facility of application, we develop the procedure of the QFD-based PL-ORESTE method. Given that the “shared cars” is a new industry appeared in Chinese market, we finally illustrate the applicability of the proposed method by a case study concerning the selection of innovative designs of Panda shared cars. [ABSTRACT FROM AUTHOR]

• We propose an aggregation model to adjust the compensation effect among criteria. • We introduce three mutually independent coefficients in Dempster's rule. • We consider the reliability of information in the model. • We validate the method by a case study of the entry screening of SMEs. The evaluation of star-up small and medium-sized enterprises (SMEs) is an important part of ensuring the success of development, but it is not an easy task because of the multiple criteria to be considered and uncertainties caused by incomplete decision information, limited individual perceptions and differences in perceptions of experts. By integrating Dempster-Shafer theory into a probabilistic linguistic setting that takes into account the uncertainties in the evaluation process by combining linguistic terms with subjective probabilities, this study proposes a multi-criteria decision-making model for the management of SMEs. We incorporate different decision factors regarding (a) the risk attitude of decision makers, (b) the relative importance of criteria, (c) the unreliability and incompleteness of evaluation on alternatives in one preference model of multi-criteria decision making. The applicability of the proposed preference model is validated by a case study of the entry screening of SMEs for business incubators. Through comparative analysis and sensitive analysis, the reliability of the proposed model is verified, which can not only express uncertain evaluation information, but also avoid information loss and defuzzification in the aggregation process. [ABSTRACT FROM AUTHOR]

Occurred on May 12, 2008, the Wenchuan earthquake in China has received worldwide attention for its incredible destructive power. In June of the same year, the State Council issued a draft regulation on post-quake restoration and reconstruction. To find the effect of post-earthquake reconstruction, this paper measures the residents' life satisfaction in Wenchuan by multiple criteria decision-making methods under probabilistic linguistic environment. Based on field surveys and domains of life satisfaction, an indicator system is proposed as a framework to collect resident's evaluations. To retain as much original information as possible, an effective weighting method with probabilistic linguistic information is developed to deduce the indicator's importance. It extends the logarithm-multiplicative analytic hierarchy process to handle the probabilistic linguistic evaluations and derive the criteria weights in interval forms. In this way, the inherent uncertainty of the evaluations on the importance of life satisfaction indicators can be reflected. Furthermore, a novel outranking method named the probabilistic linguistic gain and lost dominance score method, is used to obtain the overall life satisfaction degrees of the residents in Wenchuan. We apply this method to measure the life satisfaction degrees of residents in two areas. The calculation results show that the life satisfaction degrees of the residents in the earthquake-hit area are higher than those of the residents in the non-earthquake area. [ABSTRACT FROM AUTHOR]