This paper proposes a novel approach to cope with the multi-criteria group decision-making problems. We give the pairwise comparisons based on the best-worst-method (BWM), which can decrease comparison times. Additionally, our comparison results are determined with the positive and negative aspects. In order to deal with the decision matrices effectively, we consider the elimination and choice translation reality (ELECTRE III) method under the intuitionistic multiplicative preference relations environment. The ELECTRE III method is designed for a double-automatic system. Under a certain limitation, without bothering the decision-makers to reevaluate the alternatives, this system can adjust some special elements that have the most influence on the group's satisfaction degree. Moreover, the proposed method is suitable for both the intuitionistic multiplicative preference relation and the interval valued fuzzy preference relations through the transformation formula. An illustrative example is followed to demonstrate the rationality and availability of the novel method.
Efficient utilization of human resources is an important force for the sustainable development of society and the economy. Against the backdrop of the development of economic globalization, the Chinese Government is presently implementing the strategy of "Strengthening the Nation with Talent" to assist the exploitation and management of human resources. Overseas talents have recently become an important resource. How to scientifically evaluate and classify overseas talents has become an important research topic, and it is necessary to seek a systematic decision aid. This paper introduces a novel methodology to evaluate and classify overseas talents in China under the intuitionistic relations environment. Firstly, we determine the weighted values of decision makers and criteria through defining geometry consistency. Secondly, we construct a non-linear Best-Worst-Method (BWM) model with intuitionistic preference relations. A highlight of this BWM model for intuitionistic relations is taking both positive and negative aspects into consideration, which is different from the original BWM. Finally, the proposed methodology is applied to an illustrative example of overseas talent evaluation, indicating the simultaneous efficiency and practicability of the method.
With the increasing number of overseas talent tasks in China, overseas talent and job fit are significant issues that aim to improve the utilization of this key human resource. Many studies based on fuzzy sets have been conducted on this topic. Among the many fuzzy set methods, intuitionistic fuzzy sets are usually utilized to express and handle the evaluation information. In recent years, various intuitionistic fuzzy decision-making methods have been rapidly developed and used to solve evaluation problems, but none of them can be used to solve the person-job fit problem with intuitionistic best-worst method (BWM) and TOPSIS methods considering large-scale group decision making (LSGDM) and evaluator social network relations (SNRs). Therefore, to solve problems of intuitionistic fuzzy information analysis and the LSGDM for high-level overseas talent and job fit, we construct a new hybrid two-sided matching method named I-BTM and an LSGDM method considering SNRs. On the one hand, to express the decision-making information more objectively and reasonably, we combine the BWM and TOPSIS in an intuitionistic environment. Additionally, we develop the LSGDM with optimized computer algorithms, where the evaluators’ attitudes are expressed by hesitant fuzzy language. Finally, we build a model of high-level overseas talent and job fit and establish a mutual criteria system that is applied to a case study to illustrate the efficiency and reasonableness of the model.
Let n(k, l, m), k ≤ l ≤ m, be the smallest integer such that any finite planar point set of at least n(k, l, m) points in general position, contains an empty convex k-hole; an empty convex l-hole and an empty convex m-hole, which are all pairwise disjoint. In this paper we prove that n(3, 3, 5) = 12.
During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research.
This study systematically analyzes the impact of China’s maize Green Total Factor Productivity (GTFP) and mechanization on GTFP, providing a reference for reasonably playing the role of mechanization and improving China’s agricultural GTFP. Based on the difference in crop types and regional applicability of agricultural mechanization, this study selects maize as the research crop to analyze the impact of agricultural mechanization level on GTFP. In this study, the SBM-ML model is used to measure China’s maize GTFP, reveal the temporal and regional change characteristics of maize GTFP, and clarify the optimization direction of maize GTFP from the perspective of regional differences and resource endowment differences. This study uses the threshold regression model to systematically analyze the impact of agricultural mechanization on GTFP and its mechanism. Results are given as follows: (1) The growth of China’s maize production GTFP fluctuates greatly in each year, and the growth of maize GTFP depends on the alternate promotion of technical efficiency and technical progress. Greenhouse gas emissions have a significant impact on GTFP. Excessive use of pesticides and fertilizers is the biggest obstacle to the improvement of maize GTFP. (2) There are also specific regional differences in the factors that affect the improvement of maize GTFP efficiency in different regions. The impact of mechanization on agricultural GTFP varies among regions. (3) The development level of agricultural mechanization at different stages has different promotion effects on maize GTFP. Agricultural mechanization has a two-way effect on maize GTFP. The factors of land type and land area will not limit the promotion of agricultural mechanization to maize GTFP. (4) Agricultural financial investment, environmental pollution control efforts, agricultural science and technology expenditure and other factors play a positive role in improving GTFP. (5) In future production, we should pay attention to the combination of agricultural mechanization and regional production characteristics, optimize the allocation of agricultural machinery, and strengthen the coordination between agricultural mechanization and moderate scale operation. The findings of our study provide useful policy implications for the promotion and development of agriculture in China.
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