Special Issue "Soft Computing with Applications to Decision Making and Data Mining"

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 12790

Special Issue Editors

Prof. Dr. Feng Feng
E-Mail Website
Guest Editor
Department of Applied Mathematics, School of Science, Xian University of Posts and Telecommunications, Xi’an 710121, China
Interests: soft computing; data mining; decision making; mathematical theories for modeling uncertainty; granular computing
Dr. Harish Garg
E-Mail Website
Guest Editor
School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala 147004, Punjab, India
Interests: computational intelligence; multi-criteria decision-making; evolutionary algorithms; expert systems and decision support systems; computing with words and soft computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. G. Muhiuddin
E-Mail Website
Guest Editor
Department of Mathematics, University of Tabuk, Tabuk 71491, Saudi Arabia
Interests: ordered sets; algebras related to logic; bci-algebras; fuzzy lattices (soft algebras) and related topics

Special Issue Information

Dear Colleagues,

Soft computing refers to a collection of nonclassical computing tools, which aim to exploit tolerance for uncertainty, imprecision, and partial truth to achieve a low solution cost, tractability, and robustness. With the rise of an intelligent era, various fields in modern science and human society are permeated by data arising from perception, measurement, communication, and computation. To extract useful knowledge from collected data and make optimal decisions under uncertainty, soft computing has become more important than ever. On one hand, knowledge extraction from a huge amount of data can often serve as a fundamental basis for intelligent decision making in many fields, ranging from medicine to engineering. On the other hand, perspectives from soft computing can offer deep insight into developing novel approaches to big data analytics and approximate reasoning. This Special Issue will be devoted to state-of-the-art research on soft computing and related applications to decision making and data mining. The guest editors would like to provide a platform to present the latest advances in all aspects of soft computing, from mathematical foundation to practical applications with an emphasis on decision making and data mining. Among the topics that this Special Issue will address, we may consider the following non-exhaustive list:

soft computing; granular computing; fuzzy sets and their extensions; soft sets and their extensions; rough sets and their extensions; mathematical theory for modeling uncertainty; hybrid soft computing models; fuzzy modelling; fuzzy decision making; fuzzy decision support systems; fuzzy recommender systems; fuzzy optimization; fuzzy graphs and their extensions; decision making under uncertainty; three-way decision; data mining; pattern mining; association rules; approximate reasoning; non-classical logic; algebraic topics related to soft computing; etc.

We hope that this initiative will be attractive to researchers specialized in the above-mentioned fields. Contributions may be submitted on a continuous basis before the deadline. After a peer-review process, submissions will be selected for publication based on their quality and relevance.

Prof. Dr. Feng Feng
Dr. Harish Garg
Prof. Dr. G. Muhiuddin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soft computing
  • decision making
  • data mining
  • granular computing
  • fuzzy modelling
  • fuzzy graphs
  • fuzzy systems
  • computational intelligence
  • uncertainty modeling
  • association rules
  • approximate reasoning
  • non-classical logic

Published Papers (15 papers)

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Research

Article
Research on Fuzzy Temporal Event Association Mining Model and Algorithm
Axioms 2023, 12(2), 117; https://doi.org/10.3390/axioms12020117 - 23 Jan 2023
Viewed by 189
Abstract
As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute [...] Read more.
As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
Article
Three-Way Fuzzy Sets and Their Applications (III)
Axioms 2023, 12(1), 57; https://doi.org/10.3390/axioms12010057 - 03 Jan 2023
Viewed by 505
Abstract
Three-way fuzzy inference is the theoretical basis of three-way fuzzy control. The proposed TCRI method is based on a Mamdani three-way fuzzy implication operator and uses one inference and simple composition operation. In order to effectively improve the TCRI method, this paper proposes [...] Read more.
Three-way fuzzy inference is the theoretical basis of three-way fuzzy control. The proposed TCRI method is based on a Mamdani three-way fuzzy implication operator and uses one inference and simple composition operation. In order to effectively improve the TCRI method, this paper proposes a full implication triple I algorithm for three-way fuzzy inference and gives the triple I solution to the TFMP problem. The emphasis of our research is R0 and Go¨del triple I solution, which is related to three-way residual implication, as well as Zadeh’s and Mamdani’s triple I solution, which is based on three-way fuzzy implication operator. Then the three-way fuzzy controller is constructed by the proposed Zadeh’s and R0 triple I algorithm. Finally, the proposed triple I algorithm is applied to the three-way fuzzy control system, and its advantage is illustrated by the three-dimensional surface diagram of the control variable. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Interval-Valued General Residuated Lattice-Ordered Groupoids and Expanded Triangle Algebras
Axioms 2023, 12(1), 42; https://doi.org/10.3390/axioms12010042 - 30 Dec 2022
Viewed by 626
Abstract
As an extension of interval-valued pseudo t-norms, interval-valued pseudo-overlap functions (IPOFs) play a vital role in solving interval-valued multi-attribute decision making problems. However, their corresponding interval-valued algebraic structure has not been studied yet. On the other hand, with the development of non-commutative (non-associative) [...] Read more.
As an extension of interval-valued pseudo t-norms, interval-valued pseudo-overlap functions (IPOFs) play a vital role in solving interval-valued multi-attribute decision making problems. However, their corresponding interval-valued algebraic structure has not been studied yet. On the other hand, with the development of non-commutative (non-associative) fuzzy logic, the study of residuated lattice theory is gradually deepening. Due to the conditions of operators being weakened, the algebraic structures are gradually expanding. Therefore, on the basis of interval-valued residuated lattice theory, we generalize and research the related contents of interval-valued general, residuated, lattice-ordered groupoids. In this paper, the concept of interval-valued, general, residuated, lattice-ordered groupoids is given, and some examples are presented to illustrate the relevance of IPOFs to them. Then, in order to further study them, we propose the notions of expanded, interval-valued, general, residuated lattice-ordered groupoids and expanded triangle algebras, and explain that there is one-to-one correspondence between them through a specific proposition. Some of their properties are also analyzed. Lastly, we show the definitions of the filters on the expanded triangle algebras, and investigate the congruence and quotient structure through them. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Latent Multi-View Semi-Nonnegative Matrix Factorization with Block Diagonal Constraint
Axioms 2022, 11(12), 722; https://doi.org/10.3390/axioms11120722 - 12 Dec 2022
Viewed by 557
Abstract
Multi-view clustering algorithms based on matrix factorization have gained enormous development in recent years. Although these algorithms have gained impressive results, they typically neglect the spatial structures that the latent data representation should have, for example, the ideal data representation owns a block [...] Read more.
Multi-view clustering algorithms based on matrix factorization have gained enormous development in recent years. Although these algorithms have gained impressive results, they typically neglect the spatial structures that the latent data representation should have, for example, the ideal data representation owns a block structure just like the indicator matrix has. To address this issue, a new algorithm named latent multi-view semi-nonnegative matrix factorization with block diagonal constraint (LMSNB) is proposed. First, latent representation learning and Semi-NMF are combined to get a lower-dimensional representation with consistent information from different views. Second, the block diagonal constraint is able to capture the global structure of original data. In addition, the graph regularization is considered in our model to preserve the local structure. LMSNB can deal with negative data matrix and be applied to more fields. Although the low dimensional representation from semi-nonnegative matrix factorization loses some valuable information, it still has same structure as original data with the help of block diagonal constraint and graph regularization. Finally, an iterative optimization algorithm is proposed for our objective problem. Experiments on several multi-view benchmark datasets demonstrate the effectiveness of our approach against other state-of-the-art methods. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Chromatic Number of Fuzzy Graphs: Operations, Fuzzy Graph Coloring, and Applications
Axioms 2022, 11(12), 697; https://doi.org/10.3390/axioms11120697 - 05 Dec 2022
Viewed by 288
Abstract
We focus on fuzzy graphs with crisp vertex sets and fuzzy edge sets. This paper introduces a new concept of chromatic number (crisp) for a fuzzy graph G˜(V,E˜). Moreover, we define the operations of cap, [...] Read more.
We focus on fuzzy graphs with crisp vertex sets and fuzzy edge sets. This paper introduces a new concept of chromatic number (crisp) for a fuzzy graph G˜(V,E˜). Moreover, we define the operations of cap, join, difference, ring sum, direct product, semiproduct, strong product, and Cartesian product of fuzzy graphs. Furthermore, the exact value or the upper boundary of the chromatic number of these fuzzy graphs is obtained based on the α-cuts of G˜. Finally, two applications of the chromatic number to solve the timetabling problem and the traffic light problem are analyzed. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
MEM and MEM4PP: New Tools Supporting the Parallel Generation of Critical Metrics in the Evaluation of Statistical Models
Axioms 2022, 11(10), 549; https://doi.org/10.3390/axioms11100549 - 12 Oct 2022
Viewed by 598
Abstract
This paper describes MEM and MEM4PP as new Stata tools and commands. They support the automatic reporting and selection of the best regression and classification models by adding supplemental performance metrics based on statistical post-estimation and custom computation. In particular, MEM provides helpful [...] Read more.
This paper describes MEM and MEM4PP as new Stata tools and commands. They support the automatic reporting and selection of the best regression and classification models by adding supplemental performance metrics based on statistical post-estimation and custom computation. In particular, MEM provides helpful metrics, such as the maximum acceptable variance inflation factor (maxAcceptVIF) together with the maximum computed variance inflation factor (maxComputVIF) for ordinary least squares (OLS) regression, the maximum absolute value of the correlation coefficient in the predictors’ correlation matrix (maxAbsVPMCC), the area under the curve of receiving operator characteristics (AUC-ROC), p and chi-squared of the goodness-of-fit (GOF) test for logit and probit, and also the maximum probability thresholds (maxProbNlogPenultThrsh and maxProbNlogLastThrsh) from Zlotnik and Abraira risk-prediction nomograms (nomolog) for logistic regressions. This new tool also performs the automatic identification of the list of variables if run after most regression commands. After simple successive invocations of MEM (in a .do file acting as a batch file), the collectible results are produced in the console or exported to specially designated files (one .csv for all models in a batch). MEM4PP is MEM’s version for parallel processing. It starts from the same batch (the same .do file with its path provided as a parameter) and triggers different instances of Stata to parallelly generate the same results (one .csv for each model in a batch). The paper also includes some examples using real-world data from the World Values Survey (the evidence between 1981 and 2020, version number 1.6). They help us understand how MEM and MEM4PP support the testing of predictor independence, reverse causality checks, the best model selection starting from such metrics, and, ultimately, the replication of all these steps. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Evaluating Lean Facility Layout Designs Using a BWM-Based Fuzzy ELECTRE I Method
Axioms 2022, 11(9), 447; https://doi.org/10.3390/axioms11090447 - 31 Aug 2022
Cited by 1 | Viewed by 626
Abstract
Selecting a suitable design for a lean facility layout has become a critical issue for a manufacturing company aiming to remove waste or non-value-added activities and implement the optimal facility arrangement. Many quantitative and qualitative criteria with different weights need to be considered [...] Read more.
Selecting a suitable design for a lean facility layout has become a critical issue for a manufacturing company aiming to remove waste or non-value-added activities and implement the optimal facility arrangement. Many quantitative and qualitative criteria with different weights need to be considered in evaluating lean facility layout designs. To address the issue, a Best-Worst method (BWM) based on fuzzy ELECTRE I is introduced to determine the optimal lean facility layout design, in which the BWM is utilized for generating the criteria weights, and an extension of fuzzy ELECTRE I is introduced to identify the most suitable alternative. The signed distance method is employed to defuzzify the fuzzy numbers and obtain discordance matrix values. Based on the subtraction of discordance values from concordance values, a modified fuzzy ELECTRE I is introduced to evaluate alternative lean facility layout designs that can avoid missing information. A numerical example of the evaluation of lean facility layout designs for a manufacturing company is provided to show the potential of the suggested models. Comparative studies are investigated to illustrate the superiority of the suggested method. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
Article
Feature Selection Methods for Extreme Learning Machines
Axioms 2022, 11(9), 444; https://doi.org/10.3390/axioms11090444 - 30 Aug 2022
Viewed by 574
Abstract
Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own [...] Read more.
Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical feature selection method for their special mechanism. In this study, we proposed a feature selection method for the ELM, named FELM. The proposed algorithm achieves highly efficient dimensionality reduction due to the feature ranking strategy. The FELM can simultaneously complete the feature selection and classification processes. In addition, by incorporating a memorization–generalization kernel into the FELM, the nonlinear case of it is issued (called FKELM). The FKELM can achieve high classification accuracy and extensive generalization by applying the property of memorization of training data. According to the experimental results on different artificial and benchmark datasets, the proposed algorithms achieve significantly better classification accuracy and faster training than the other methods. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
A Spherical Fuzzy Multi-Criteria Decision-Making Model for Industry 4.0 Performance Measurement
Axioms 2022, 11(7), 325; https://doi.org/10.3390/axioms11070325 - 04 Jul 2022
Cited by 3 | Viewed by 773
Abstract
In recent years, efficient processes have become increasingly important because of high-level competition in the production industry. The concept of Industry 4.0 is a relatively new and effective method for managing production processes. Because the Industry 4.0 implementation process includes connections between objects, [...] Read more.
In recent years, efficient processes have become increasingly important because of high-level competition in the production industry. The concept of Industry 4.0 is a relatively new and effective method for managing production processes. Because the Industry 4.0 implementation process includes connections between objects, humans, and systems, it is quite difficult to evaluate and measure the performance. At this stage, performance criteria can be applied. However, linguistic evaluation of criteria makes the problem too complicated to solve. The purpose of this paper is to present a novel fuzzy performance measurement model for Industry 4.0 in small and medium-sized manufacturing firms. A hybrid spherical fuzzy analytic hierarchy process (SF-AHP)—weighted score methodology (WSM) is proposed for the performance measurement and scoring process. In the application part of this paper, the propounded methodology was applied to five companies. The results of this study can be used as a reference for experts in the performance measurement of the Industry 4.0 process. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Information Measures Based on T-Spherical Fuzzy Sets and Their Applications in Decision Making and Pattern Recognition
Axioms 2022, 11(7), 302; https://doi.org/10.3390/axioms11070302 - 21 Jun 2022
Cited by 2 | Viewed by 642
Abstract
The T-spherical fuzzy set (TSFS) is a modification of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), q-rung orthopair fuzzy set (q-ROFS), and picture fuzzy set (PFS), with three characteristic functions: the membership degree (MD) denoted by S, [...] Read more.
The T-spherical fuzzy set (TSFS) is a modification of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), q-rung orthopair fuzzy set (q-ROFS), and picture fuzzy set (PFS), with three characteristic functions: the membership degree (MD) denoted by S, the nonmembership degree (NMD) denoted by D, and the abstinence degree (AD) denoted by I. It can be used to solve problems of uncertain information with no restrictions. The distance measure (DM) is a tool that sums up the difference between points, while the similarity measure (SM) is a method applied to calculate the similarity between objects within an interval of [0,1]. The current work aims to introduce novel DMs and SMs in the environment of TSFSs to show the limitations of the previously defined DMs and SMs. The suggested DMs and SMs provide more room for all three degrees to be selected without restriction. We investigated the effectiveness of the proposed DMs and SMs by applying a pattern-recognition technique, and we determined their applicability for multicriteria decision making (MCDM) using numerical examples. The newly proposed DMs and SMs are briefly compared to existing DMs and SMs, and appropriate conclusions are drawn. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Public Opinion Spread and Guidance Strategy under COVID-19: A SIS Model Analysis
Axioms 2022, 11(6), 296; https://doi.org/10.3390/axioms11060296 - 17 Jun 2022
Cited by 5 | Viewed by 1259
Abstract
Both the suddenness and seriousness of COVID-19 have caused a variety of public opinions on social media, which becomes the focus of social attention. This paper aims to analyze the strategies regarding the prevention and guidance of public opinion spread under COVID-19 in [...] Read more.
Both the suddenness and seriousness of COVID-19 have caused a variety of public opinions on social media, which becomes the focus of social attention. This paper aims to analyze the strategies regarding the prevention and guidance of public opinion spread under COVID-19 in social networks from the perspective of the emotional characteristics of user texts. Firstly, a model is established to mine text-based emotional tendency based on the Susceptible-Infectious-Susceptible (SIS) model. In addition, a mathematical and simulation analysis of the model is presented. Finally, an empirical study based on the data of microblog contents regarding COVID-19 public opinion in the Sina Weibo platform from January to March 2020 is conducted to analyze the factors that boost and hinder COVID-19 public opinion. The results show that when positive emotion is higher than 0.8, the spread of negative public opinion can be blocked. When the negative emotion and neutral emotion are both below 0.2, the spread of COVID-19 public opinion would be weakened. To accurately guide public opinion on COVID-19, the government authorities should establish a public opinion risk evaluation and an early warning mechanism. Platforms should strengthen public opinion supervision and users should improve their media literacy. The media organizations should insist on positive reporting, improve social cohesion, and guide the trend of public opinion. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast
Axioms 2022, 11(6), 292; https://doi.org/10.3390/axioms11060292 - 15 Jun 2022
Cited by 2 | Viewed by 809
Abstract
Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training [...] Read more.
Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
A Novel Multi-Criteria Decision-Making Method Based on Rough Sets and Fuzzy Measures
Axioms 2022, 11(6), 275; https://doi.org/10.3390/axioms11060275 - 06 Jun 2022
Cited by 10 | Viewed by 860
Abstract
Rough set theory provides a useful tool for data analysis, data mining and decision making. For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods correspond to different rules, which will [...] Read more.
Rough set theory provides a useful tool for data analysis, data mining and decision making. For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods correspond to different rules, which will influence the decision result. To solve this problem, we propose a novel method for MCDM based on rough sets and a fuzzy measure in this paper. Firstly, a type of non-additive measure of attributes is presented by the importance degree in rough sets, which is a fuzzy measure and called an attribute measure. Secondly, for a decision information system, the notion of the matching degree between two objects is presented under an attribute. Thirdly, based on the notions of the attribute measure and matching degree, a Choquet integral is constructed. Moreover, a novel MCDM method is presented by the Choquet integral. Finally, the presented method is compared with other methods through a numerical example, which is used to illustrate the feasibility and effectiveness of our method. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
Article
A Two-Stage Multi-Criteria Supplier Selection Model for Sustainable Automotive Supply Chain under Uncertainty
Axioms 2022, 11(5), 228; https://doi.org/10.3390/axioms11050228 - 13 May 2022
Cited by 6 | Viewed by 1614
Abstract
Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to [...] Read more.
Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to ensure sustainable supply chains, recover better from the pandemic and effectively respond to any future unprecedented crises. The purpose of this study is to assess and choose a possible supplier based on their capability to adapt to the COVID-19 epidemic in a sustainable manner. For this assessment, a framework based on multi-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts. In the first stage, the evaluation criteria system is identified through a literature review and experts’ opinions. The SF-AHP is then used to determine the criteria weights. Finally, the G-COPRAS method is utilized to select sustainable suppliers. A case study in the automotive industry in Vietnam is presented to demonstrate the proposed approach’s effectiveness. From the SF-AHP findings, “quality”, “use of personal protective equipment”, “cost/price”, “safety and health practices and wellbeing of suppliers”, and “economic recovery programs” have been ranked as the five most important criteria. From G-COPRAS analysis, THACO Parts (Supplier 02) is the best supplier. A sensitivity study was also conducted to verify the robustness of the proposed model, in which the priority rankings of the best suppliers are very similar. For long-term development and increased competitiveness, industrial businesses must stress the integration of response mechanisms during SSS implementation in the COVID-19 epidemic, according to the findings. This will result in significant cost and resource savings, as well as reduced environmental consequences and a long-term supply chain, independent of the crisis. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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Article
Fuzzy Multi Criteria Decision Making Model for Agritourism Location Selection: A Case Study in Vietnam
Axioms 2022, 11(4), 176; https://doi.org/10.3390/axioms11040176 - 14 Apr 2022
Cited by 3 | Viewed by 1189
Abstract
Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose [...] Read more.
Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose the most appropriate agritourism location in Vietnam for long-term investment. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on eco-nomic, social, and environmental factors. In the first stage, the fuzzy analytic hierarchy process (FAHP) is used to estimate the relative criteria rating through the evaluation process. In the second stage, the fuzzy technique for order preference using similarities to ideal solution (FTOPSIS) is applied to rank the potential alternative locations. Finally, the best alternative to tourist site investment is Can Tho (A8), which maximizes resources and enhances the local benefits. Future research can also be used to support similar site-selection processes in other regions or could be applied to other types of tourism. Full article
(This article belongs to the Special Issue Soft Computing with Applications to Decision Making and Data Mining)
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