AN EXTENDED COPRAS MODEL FOR MULTIPLE ATTRIBUTE GROUP DECISION MAKING BASED ON SINGLE-VALUED NEUTROSOPHIC 2-TUPLE LINGUISTIC ENVIRONMENT

In this article, we develop the COPRAS model to solve the multiple attribute group decision making (MAGDM) under single-valued neutrosophic 2-tuple linguistic sets (SVN2TLSs). Firstly, we introduce the relevant knowledge about SVN2TLSs in a nutshell, such as the definition, the operation laws, a few of fused operators and so on. Then, combine the traditional COPRAS model with SVN2TLNs, and structure as well as elucidate the computing steps of the SVN2TLNCOPRAS pattern. Furthermore, in this article, we propose a method for determining attribute weights in different situations relying on the maximizing deviation method with SVN2TLNs. Last but not least, a numerical example about assessing the safety of construction project has been designed. And for further demonstrating the advantage of the new designed method, we also select a number of existed methods to have comparisons.

A VIKOR-based framework to optimize the location of fast-charging stations with proportional hesitant fuzzy information

In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal.

Evaluate Air Pollution by Promethee Ranking in Yangtze River Delta of China

A series of problems that are related to population, resources, environment, and ecology have emerged in recent years with the advancement of industrialization and urbanization in China. Especially, air pollution has become a severe trouble that directly endangers the health of residents. Accordingly, it is a need to make the assessment of air quality among cities, so that corresponding measures can be taken. For this purpose, ten major cities are selected as the research objects in Yangtze River Delta. Additionally, this study gathers and processes the data of five main air pollutants PM2.5, PM10, SO2, O3, and NO2, respectively. Furthermore, the maximizing deviation method is used to obtain the respective weight of these pollutants and the preference ranking organization method for enrichment evaluations (PROMETHEE) is introduced into the assessment of air quality among ten cities. As a result, the ranking of air quality in Ningbo, Wenzhou, Shanghai, and Shaoxing was at the fore from 2014 to 2017. Meanwhile, the performance of Ningbo has always kept the top two and Shaoxing’s ranking has risen since 2015. In addition, the air quality of Changzhou, Suzhou and Hangzhou was at an average level in the past four years. Moreover, the performance of Nanjing, Wuxi, and Zhenjiang was terrible when compared to other cities. Some useful suggestions have been proposed to control air quality based on the ranking results.

Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment

With the development of the social economy and enlarged volume of information, the application of multiple-attribute decision-making (MADM) has become increasingly complex, uncertain, and obscure. As a further generalization of hesitant fuzzy set (HFS), simplified neutrosophic hesitant fuzzy set (SNHFS) is an efficient tool to process the vague information and contains the ideas of a single-valued neutrosophic hesitant fuzzy set (SVNHFS) and an interval neutrosophic hesitant fuzzy set (INHFS). In this paper, we propose a decision-making approach based on the maximizing deviation method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to solve the MADM problems, in which the attribute weight information is incomplete, and the decision information is expressed in simplified neutrosophic hesitant fuzzy elements. Firstly, we inaugurate an optimization model on the basis of maximizing deviation method, which is useful to determine the attribute weights. Secondly, using the idea of the TOPSIS, we determine the relative closeness coefficient of each alternative and based on which we rank the considered alternatives to select the optimal one(s). Finally, we use a numerical example to show the detailed implementation procedure and effectiveness of our method in solving MADM problems under simplified neutrosophic hesitant fuzzy environment.

Tackling Complexity in Green Contractor Selection for Mega Infrastructure Projects: A Hesitant Fuzzy Linguistic MADM Approach with considering Group Attitudinal Character and Attributes’ Interdependency

Continuous environmental concerns regarding construction industry have been driving general constructors of mega infrastructure projects to incorporate green contractors. Although conventional multiple attributes decision-making (MADM) methodologies have provided feasible ways to select contractor, high complexity in scenarios of megaprojects still challenges existing MADM methods in concurrently accommodating three key issues of decision hesitancy, attributes interdependency, and group attitudinal character. To elicit decision-makers’ hesitant fuzzy assessments more objectively and comprehensively, we define an expression tool called interval-valued dual hesitant fuzzy uncertain unbalanced linguistic set (IVDHF_UUBLS) and develop aggregation operators through its operations. To exploit attributes interdependency, we establish a synthesized attributes’ weighting model to fuse an attributes interdependency-based weighting vector and an argument-dependent weighting vector, which are, respectively, derived through Decision-Making and Trial Evaluation Laboratory (DEMATEL) technique and maximizing deviation method. To effectively utilize decision-makers’ group attitudinal characters, we also develop a TOPSIS-based method to rationally transform group ideal attitudes into order-inducing vectors. On the strength of the above methods, an integrated MADM approach is then constructed. Finally, illustrative case study and experiments are conducted to validate our approach.

Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model

Customer requirement analysis has become a primary concern for companies who compete in the global market. Kano’s model, as a customer-driven tool, has been widely used for customer requirement analysis in product improvement. Although a number of authors have improved the traditional Kano’s model, there has been a limitation of dealing with the fuzzy and uncertainty of human thought under multi-granularity linguistic environment. Furthermore, the traditional Kano’s model faces problems regarding quantitative data computation and customer requirements importance assessment. In this article, an improved fuzzy Kano’s model is proposed to analyze customer requirements under uncertain environment. A 2-tuple linguistic fuzzy Kano’s questionnaire is developed to model the uncertainty and diversity of customers’ assessments using 2-tuple linguistic variables under multi-granularity linguistic environment. Then, a comprehensive and systematic methodology is presented to prioritize customer requirements through quantitative analysis of improved fuzzy Kano’s model. This method integrates subjective judgments assigned by decision maker, objective weights based on maximizing deviation method and customer satisfaction contribution to determine the priority ratings of customer requirements. A case study of combine harvester development is presented to evaluate the proposed model.