Decision-Making Method of Qualitative and Quantitative Comprehensive Evaluation of Talents Based on Probability Hesitation Fuzzy Language

A scientific, reasonable, and novel talent evaluation index system is the foundation of talent training and selection. Based on the novel “Man-Machine-Environment System Engineering” (hereinafter referred to as MMESE) theory, this paper proposes a novel talent evaluation index system that considers the ontological attributes and the external environment of the object comprehensively for talent evaluation, which could help the evaluator obtain more accurate evaluation results. Since the comprehensive evaluation of MMESE talents is a complex decision-making problem that is both qualitative and quantitative, a corresponding decision-making method that integrates qualitative and quantitative approaches is proposed here based on probabilistic language entropy and the possibility of superior order relationships. First, the weights of quantitative and qualitative attributes are calculated based on entropy theory and probabilistic fuzzy language. Second, the standard weight vectors of qualitative and quantitative attributes are obtained by adjusting the weight integration coefficients, and the change intervals of the pros and cons between the objects to be evaluated are calculated. Third, the pros and cons of the objects to be evaluated are compared to obtain the possibility degree matrix that describes the priority relationships among the objects, and a ranking vector is derived from the possibility degree matrix to reflect the rankings of the objects’ pros and cons. Finally, this system and the decision-making methods have been verified as scientific and effective.

Development of the GM(1,1,⊗b) model with a trapezoidal possibility function and its application

PurposeThe purpose of this paper is to overcome the weakness of the traditional model, in which the grey action quantity is a real number and thus leads to a “unique solution” and to build the model with a trapezoidal possibility degree function.Design/methodology/approachUsing the system input and output block diagram of the model, the interval grey action quantity is restored under the condition of insufficient system influencing factors, and the trapezoidal possibility degree function is formed. Based on that, a new model able to output non-unique solutions is constructed.FindingsThe model satisfies the non-unique solution principle of the grey theory under the condition of insufficient information. The model is compatible with the traditional model in structure and modelling results. The validity and practicability of the new model are verified by applying it in simulating the ecological environment water consumption in the Yangtze River basin.Practical implicationsIn this study, the interval grey number form of grey action quantity is restored under the condition of insufficient system influencing factors, and the unique solution to the problem of the traditional model is solved. It is of great value in enriching the theoretical system of grey prediction models.Social implicationsTaking power consumption as an example, the accurate prediction of the future power consumption level is related to the utilization efficiency of the power infrastructure investment. If the prediction of the power consumption level is too low, it will lead to the insufficient construction of the power infrastructure and the frequent occurrence of “power shortage” in the power industry. If the prediction is too high, it will lead to excessive investment in the power infrastructure. As a result, the overall surplus of power supply will lead to relatively low operation efficiency. Therefore, building an appropriate model for the correct interval prediction is a better way to solve such problems. The model proposed in this study is an effective one to solve such problems.Originality/valueA new grey prediction model with its interval grey action quantity based on the trapezoidal possibility degree function is proposed for the first time.

Multi-attribute group decision-making process based on possibility degree and operators for intuitionistic multiplicative set

This paper aims to present a novel multiple attribute group decision-making process under the intuitionistic multiplicative preference set environment. In it, Saaty’s 1/9-9 scale is used to express the imprecise information which is asymmetrical distribution about 1. To achieve it, the present work is divided into three folds. First, a concept of connection number-based intuitionistic multiplicative set (CN-IMS) is formulated by considering three degrees namely “identity”, “contrary”, and “discrepancy” of the set and study their features. Second, to rank the given number, we define a novel possibility degree measure which compute the degree of possibility within the given objects. Finally, several aggregation operators on the pairs of the given numbers are designed and investigated their fundamental inequalities and relations. To explain the presented measures and operators, a group decision-making approach is promoted to solve the problems with uncertain information and illustrated with several examples. The advantages, comparative, as well as perfection analysis of the proposed framework are furnished to confirm the approach.

Interval probability hesitant fuzzy linguistic analytic hierarchy process and its application in talent selection

Hesitant fuzzy linguistic term set (HFLTS) can handle the qualitative and hesitant information in multiple attribute decision making (MADM) problems which are widely used in various fields. However, the experts’ evaluation of information is not completely reliable in the situation where their own knowledge background is insufficient. In order to deal with deviations due to incomplete reliability of the evaluation, this paper first proposes the interval probability hesitant fuzzy linguistic variable (IPHFLV), which takes the HFLTS as the evaluation part and adds a novel element-reliability of evaluation, thus can describe the different credibility of information evaluation due to the familiarity of experts with schemes and the differences in knowledge cognition. The operation rules and comparison methods are also illustrated. Particularly, under the inspiration of probability theory, we propose the possibility degree of the IPHFLVs. Then we propose IPHFL-AHP based on the AHP and interval probability hesitant fuzzy linguistic variable. Especially, the general geometric consistency index (G-GCI) based on the unbiased estimator of the variance is presented to measure the consistency and the iterative algorithm is constructed to improve the consistency. We use the possibility degree to calculate the priority vector to acquire the total ranking and introduce the process of IPHFL-AHP. Finally, case study of talent selection is given to illustrate the effectiveness and feasibility of the proposed method.

Group DEMATEL decision method based on hesitant fuzzy linguistic term sets

Background: Decision-making trial and evaluation laboratory (DEMATEL) is a practical and concise method to deal with the complicated socioeconomic system problems. However, there are two defects in original DEMATEL. On the one hand the traditional expert preference expressions can’t reflect the hesitation and flexibility of expert, on the other hand the determination of group experts’ weight usually be expressed the equivalent weight which can’t reflect the scientificality of weight on the behalf of experts’ academic background, capability experience, risk preference and so on. To solve the above problems, a novel Group DEMATEL decision method based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. Method: Firstly, this paper presents that experts make their judgement on the causal relationship of factors by using a linguistic expression closed to human expression, which can be easily transformed into HFLTSs. Next the hybrid weight of experts are calculated on the base of the initial HFLTSs direct influence matrix (HDIM) according to the hesitant degree and distance between two HDIMs. And the aggregation of each expert’s information is introduced by possibility degree. Then the new group DEMATEL decision method based on HFLTSs are constructed. Finally, an illustrative example is given and analyzed to demonstrate the effectiveness and validation of the proposed approach. Results: This paper demonstrate the heterogeneity of decision experts and the hesitation degree of expert information representation must be taken into account when determining the interaction of factors in complex systems by DEMATEL method. Conclusion: This paper constructs the new amended group DEMATEL which provides a new way to deal with the integration of each expert’s information by the hybrid weight and possibility degree. The methods provides references for determining the importance of complex system factors more scientifically and objectively.