Hesitant fuzzy linguistic TOPSIS method using a possibility-based comparison approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets

2017 ◽  
Vol 33 (6) ◽  
pp. 3309-3322
Author(s):  
Zhiming Zhang
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Comparison Approach ◽  
Linguistic Term ◽  
Fuzzy Linguistic

Multi-criteria decision-making based on hesitant fuzzy linguistic term sets: An outranking approach

2015 ◽  
Vol 86 ◽  
pp. 224-236 ◽  
Author(s):  
Jing Wang ◽  
Jian-qiang Wang ◽  
Hong-yu Zhang ◽  
Xiao-hong Chen
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Linguistic Term ◽  
Fuzzy Linguistic

scholarly journals Sustainable Construction Supplier Selection by a Multiple Criteria Decision-making Method with Hesitant Linguistic Information

2020 ◽  
Vol 23 (4) ◽  
pp. 119-136
Author(s):  
Huchang Liao ◽  
Ruxue Ren ◽  
Jurgita Antucheviciene ◽  
Jonas Šaparauskas ◽  
Abdullah Al-Barakati
Keyword(s):  
Decision Making ◽  
Resource Constraints ◽  
Distance Measure ◽  
Multiple Criteria Decision Making ◽  
Sustainable Construction ◽  
Multi Criteria Decision Making ◽  
Evaluation Tool ◽  
Linguistic Term ◽  
Decision Making Model ◽  
Fuzzy Linguistic

Within the context of resource constraints and ecological environment imbalance, the adoption of green suppliers can help construction enterprises achieve sustainable development and improve their competitiveness. The selection of sustainable construction suppliers is a multi-criteria decision-making problem since multiple factors should be considered. The increasingly complex decision-making environment makes it difficult for evaluators to give accurate evaluation values. In this regard, the hesitant fuzzy linguistic term set is a qualitative evaluation tool to represent the comprehensive linguistic evaluation values of experts by considering the hesitancy behaviors of experts. In this paper, a scientific multi-criteria decision-making model based on the improved Stepwise Weight Assessment Ratio Analysis (SWARA) method and the double normalization-based multi-aggregation (DNMA) method in the hesitant fuzzy linguistic environment is proposed. A new distance measure is proposed to measure the differences between hesitant fuzzy linguistic term sets with different lengths without changing the original evaluation information of experts. The proposed distance measure is applied to the proposed multi-criteria decision-making model. After improving the calculation steps of the traditional SWARA method, we can determine the weights of criteria effectively through our proposed model. To verify the applicability of the proposed method, we implement it to select sustainable building suppliers. The effectiveness of the method is verified by sensitivity analysis. We also compare the results obtained by our method and those derived by the Weight Aggregated Sum Product ASsessment (WASPAS) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The proposed method have a strong applicability to solve the sustainability-related decision problems given that it can effectively determine the weights of criteria and flexibly meet the needs of decision-makers by adjusting the coefficient.

Some Approaches to Constructing Distance Measures for Hesitant Fuzzy Linguistic Term Sets with Applications in Decision-Making

2018 ◽  
Vol 17 (01) ◽  
pp. 103-132 ◽  
Author(s):  
Na Zhao ◽  
Zeshui Xu ◽  
Zhiliang Ren
Keyword(s):  
Decision Making ◽  
Distance Measures ◽  
Multi Criteria Decision Making ◽  
Comparative Analyses ◽  
Linguistic Term ◽  
Fuzzy Linguistic

The purpose of this paper is to investigate the methods of constructing distance measures for hesitant fuzzy linguistic term sets (HFLTSs) and their applications in multi-criteria decision-making (MCDM). We first present some methods to construct distance measures for HFLTSs. Then, we discuss the properties of different distance measures in detail, which are very helpful in comparison of these distance measures for HFLTSs. After that, based upon the distance measures for HFLTSs, a decision-making method is proposed to solve the MCDM problems, in which the assessment values of alternatives over criteria are HFLTSs and the weights of criteria are completely unknown or expressed by HFLTSs. Finally, an example concerning the evaluation of movie quality is provided to demonstrate the practicability of the proposed method, and necessary comparative analyses are conducted to illustrate the effectiveness and advantages of the developed distance measures and the proposed decision-making method.

scholarly journals The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Liuxin Chen ◽  
Xiaoling Gou
Keyword(s):  
Decision Making ◽  
Score Function ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Linguistic Terms ◽  
Complete Representation ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
Todim Method

AbstractProbabilistic linguistic term sets (PLTSs) play an important role in multi-criteria decision-making(MCDM) problems because it can not only describe objects with several possible linguistic terms, but also represent the proportion of each linguistic term, which can effectively avoid the distortion of decision information to a greater extent and ensure the credibility of decision results. First, to compare PLTS more simply and reasonably, we define a new score function that takes into account partial deviations. Then considering the superiority of the classic combinative distance-based assessment (CODAS) method in the complete representation of information, it is extended to the probabilistic linguistic environment. Subsequently, we improved the classic CODAS method and proposed the PL-CODAS method. Finally, we apply the PL-CODAS method to a cases of venture investors choosing emerging companies, and we compare the proposed method with PL-TOPSIS method, PL-TODIM method and PL-MABAC method to verify its applicability and effectiveness.

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