A two-stage similarity clustering-based large group decision-making method with incomplete probabilistic linguistic eval
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METHODOLOGIES AND APPLICATION
A two-stage similarity clustering-based large group decision-making method with incomplete probabilistic linguistic evaluation information Xuanhua Xu1 • Yuzhou Hou1
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Jishan He1 • Zitao Zhang1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In recent years, probabilistic linguistic term set (PLT) is widely used in large group decision making (LGDM) for its integrity. However, the complexity of probabilistic linguistic LGDM and the large span of experts’ profession cause two problems. On the one hand, it is difficult for all experts to give complete evaluation information in the form of PLTs. For this, we propose an expertise-based probabilistic linguistic evaluation information complement method. First, we identify authoritative experts under each attribute through professional hesitation and professional consistency. Then, we establish an optimization function to obtain the optimal missing value through the expectation score of authoritative experts and the linguistic term using habit of pending experts. On the other hand, the similarity between two experts cannot be fully represented by the sum of the distance of expert evaluation value. For this, we propose a two-stage similarity measurement method and introduce the distance weighting process, which not only measures the similarity between two expert evaluation values, but also measures the difference in degree of distance between two experts under different attributes. Finally, we apply this LGDM method to hot dry rock exploration site selection in southeast coast of China. Keywords Incomplete probabilistic linguistic evaluation information Expert expertise Two-stage similarity measurement Large group decision making
1 Introduction In recent years, with the rapid development of global economy, the expanding energy demand makes the development of new clean energy an urgent task of China. Geothermal energy is recycled, while its distribution is extensive. Hot dry rock (HDR) is a kind of new geothermal energy, accounting for about 30% (Wang et al. 2018) of the proved geothermal resources, much higher than the traditional fossil energy. The economically developed southeast coastal area of China is located in the abundance area of geothermal resources. The site selection of HDR Communicated by V. Loia. & Yuzhou Hou [email protected] Xuanhua Xu [email protected] 1
School of Business, Central South University, Changsha, Hunan, China
exploration involves many subjects such as geology, energy, environment, security and economy. Besides, the exploration site selection evaluation index is difficult to quantify, such as rock type and land utilization. Therefore, it can be treated as a multi-attribute group decision-making (MAGDM) problem. We can invite expert from different fields to participate in the decision making, gather wisdom and collect the professional opinions from every experts, so as to improve the rationality and reliability of the decision making. Consid
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