scholarly journals Combined probabilistic linguistic term set and ELECTRE II method for solving a venture capital project evaluation problem

2021 ◽  
pp. 1-23
Author(s):  
Feng Shen ◽  
Chen Liang ◽  
Zhiyuan Yang
Keyword(s):  
Venture Capital ◽  
Project Evaluation ◽  
Probabilistic Linguistic Term Set ◽  
Linguistic Term ◽  
Evaluation Problem

scholarly journals A Group Decision Framework for Renewable Energy Source Selection under Interval-Valued Probabilistic linguistic Term Set

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 986 ◽  
Author(s):  
Raghunathan Krishankumar ◽  
Arunodaya Raj Mishra ◽  
Kattur Soundarapandian Ravichandran ◽  
Xindong Peng ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Group Decision ◽  
Decision Framework ◽  
Probabilistic Linguistic Term Set ◽  
Source Selection ◽  
Energy Assessment ◽  
Linguistic Term ◽  
Criteria Weights ◽  
Interval Valued

In recent years, the assessment of desirable renewable energy alternative has been an extremely important concern that could change the environment and economic growth. To tackle the circumstances, some authors have paid attention to selecting the desirable renewable energy option by employing the decision-making assessment and linguistic term sets. With a fast-growing interest in multi-criteria group decision-making (MCGDM) problems, researchers are tirelessly working towards new techniques for better decision-making. Decision makers (DMs) generally rate alternatives linguistically with different probabilities occurring for each term. Previous studies on linguistic decision-making have either ignored this idea or have used an only a single value for representing the weight of the linguistic term. Since expression of the complete probability distribution is hard and implicit hesitation exists, representation of weights of the linguistic terms using a single value becomes imprecise and unreasonable. To avoid this challenge, an interval-valued probabilistic linguistic term set (IVPLTS) is used, which is a generalization of (probabilistic linguistic term set) PLTS. Inspired by the usefulness of IVPLTS concept, we develop a decision framework for rational decision making. Initially, some operational laws and axioms are presented. Further, a novel aggregation operator known as interval-valued probabilistic linguistic simple weighted geometry (IVPLSWG) is developed for aggregating DMs’ preferences. Also, criteria weights are determined using the newly developed interval-valued probabilistic linguistic standard variance (IVPLSV) approach and alternatives are ranked using the extended VIKOR (VlseKriterijumskaOptimizacijaKompromisnoResenje) method under IVPLTS environment. Finally, a numerical example of renewable energy assessment is demonstrated to show the practicality of the developed decision framework. Also, the strengths and weaknesses of the developed decision framework are illustrated by comparison with existing ones.

scholarly journals Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 954
Author(s):  
Aiwu Zhao ◽  
Junhong Gao ◽  
Hongjun Guan
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Exchange ◽  
Forecasting Model ◽  
Historical Time ◽  
Logical Relationship ◽  
Probabilistic Linguistic Term Set ◽  
Time Series Dataset ◽  
Proposed Model ◽  
Linguistic Term

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

scholarly journals A NOVEL DECISION-MAKING FRAMEWORK BASED ON PROBABILISTIC LINGUISTIC TERM SET FOR SELECTING SUSTAINABLE SUPPLIER CONSIDERING SOCIAL CREDIT

2021 ◽  
Vol 0 (0) ◽  
pp. 1-34
Author(s):  
Yuanxiang Dong ◽  
Xumei Zheng ◽  
Zeshui Xu ◽  
Weijie Chen ◽  
Hongbo Shi ◽  
...  
Keyword(s):  
Decision Making ◽  
Success Factors ◽  
Chinese Government ◽  
Relative Importance ◽  
Credit System ◽  
Probabilistic Linguistic Term Set ◽  
Social Credit ◽  
Sustainable Supplier Selection ◽  
Linguistic Term ◽  
Decision Making Framework

Sustainable supplier selection (SSS) has become an essential task for decision-makers in competitive environments. We construct a new decision-making framework for SSS. First, classical SSS usually includes fixed factors in environmental, social and economic dimensions. Differently, we adopt new social factors from credit perspective with corporate social credit system being promoted vigorously by the Chinese government. Next, we employ probabilistic linguistic term sets (PLTSs) to collect experts’ judgments about interactive influence between factors. Third, we combine PLTSs with Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify critical success factors (CSFs) for improving decision-making efficiency. And we also give definition to relative importance degree, standard relative importance degree, deviation of importance degree and influence degree to reflect the interactive influence between factors. To eliminate subjective influence, we combine entropy weighting approach and DEMATEL to compute weights. Fourthly, we redefine dominance degree and apply it into TODIM method for SSS. Finally, the proposed decision-making framework’s effectiveness is verified by using the case study of a new energy vehicle (NEV) company. Based on this, sensitivity analysis and comparison of methods are conducted. The results verify that the decision-making framework is valid and effective for SSS.

scholarly journals Combination of Probabilistic Linguistic Term Sets and PROMETHEE to Evaluate Meteorological Disaster Risk: Case Study of Southeastern China

2019 ◽  
Vol 11 (5) ◽  
pp. 1405 ◽  
Author(s):  
Xiaobing Yu ◽  
Hong Chen ◽  
Zhonghui Ji
Keyword(s):  
Disaster Risk ◽  
Probabilistic Linguistic Term Set ◽  
Meteorological Disaster ◽  
Evaluation Approach ◽  
De Algorithm ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
Promethee Method ◽  
Emergency Measures

The meteorological disasters have brought destructive damages all around the world in the past decades. These disasters have posed great threats to sustainable development. It is necessary to evaluate meteorological disaster risk to make corresponding emergency measures. The process is uncertain and fuzzy regarding the experts’ preferences. To deal with the problem, a novel evaluation approach based on PROMETHEE method and probabilistic linguistic term set (PLTS) is firstly proposed. First of all, PLTS is adopted to express preferences’ of experts. Then, the weights of criteria are obtained by the differential evolution (DE) algorithm, and steps of the method are proposed. Finally, the proposed method is used to evaluate the whole meteorological disaster risk in the southeast coastal areas of China and results have verified the effectiveness of the method. By comparing with some similar methods, results have demonstrated the advantages of the approach.

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