Mathematics

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18 pages, 693 KiB

Open AccessArticle

Fuzzy Ranking Network DEA with General Structure

by Plácido Moreno and Sebastián Lozano

Mathematics 2020, 8(12), 2222; https://doi.org/10.3390/math8122222 - 14 Dec 2020

Cited by 2 | Viewed by 1572

This paper extends two fuzzy ranking data envelopment analysis (DEA) approaches to the case of general networks of processes. The first approach provides an efficiency score for each possibility level which requires solving one linear program for each possibility level. The second approach [...] Read more.

This paper extends two fuzzy ranking data envelopment analysis (DEA) approaches to the case of general networks of processes. The first approach provides an efficiency score for each possibility level which requires solving one linear program for each possibility level. The second approach is even simpler and provides an overall efficiency score solving just one linear program. The proposed approaches are tested on two datasets from the literature and compared with other fuzzy network DEA approaches. The results show that the two methods provide very highly correlated efficiency estimates which are also consistent with those of other fuzzy network DEA approaches. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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27 pages, 4479 KiB

Open AccessArticle

Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints

by Chia-Nan Wang, Thanh-Tuan Dang, Tran Quynh Le and Panitan Kewcharoenwong

Mathematics 2020, 8(12), 2109; https://doi.org/10.3390/math8122109 - 26 Nov 2020

Cited by 13 | Viewed by 4920

Abstract

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize [...] Read more.

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO₂ emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO₂ emissions in the objective function. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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16 pages, 1018 KiB

Open AccessFeature PaperArticle

Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

by Seohee Park, Seongeun Kim and Ji Hoon Ryoo

Mathematics 2020, 8(11), 2076; https://doi.org/10.3390/math8112076 - 20 Nov 2020

Cited by 2 | Viewed by 2292

Abstract

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and [...] Read more.

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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30 pages, 1948 KiB

Open AccessArticle

Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals

by M. G. Brikaa, Zhoushun Zheng and El-Saeed Ammar

Mathematics 2020, 8(3), 305; https://doi.org/10.3390/math8030305 - 25 Feb 2020

Cited by 21 | Viewed by 2098

Abstract

The intuitionistic fuzzy set (IFS) is applied in various decision-making problems to express vagueness and showed great success in realizing the day-to-day problems. The principal aim of this article is to develop an approach for solving multi-criteria matrix game with intuitionistic fuzzy (I-fuzzy) [...] Read more.

The intuitionistic fuzzy set (IFS) is applied in various decision-making problems to express vagueness and showed great success in realizing the day-to-day problems. The principal aim of this article is to develop an approach for solving multi-criteria matrix game with intuitionistic fuzzy (I-fuzzy) goals. The proposed approach introduces the indeterminacy resolving functions of I-fuzzy numbers and discusses the I-fuzzy inequalities concept. Then, an effective algorithm based on the indeterminacy resolving algorithm is developed to obtain Pareto optimal security strategies for both players through solving a pair of multi-objective linear programming problems constructed from two auxiliary I-fuzzy programming problems. It is shown that this multi-criteria matrix game with I-fuzzy goals is an extension of the multi-criteria matrix game with fuzzy goals. Moreover, two numerical simulations are conducted to demonstrate the applicability and implementation process of the proposed algorithm. Finally, the achieved numerical results are compared with the existing algorithms to show the advantages of our algorithm. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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21 pages, 2443 KiB

Open AccessArticle

Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach

by Yu-Cheng Lin, Yu-Cheng Wang, Tin-Chih Toly Chen and Hai-Fen Lin

Mathematics 2019, 7(11), 1097; https://doi.org/10.3390/math7111097 - 13 Nov 2019

Cited by 44 | Viewed by 2067

Abstract

Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence [...] Read more.

Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence approach is proposed in this study. In the fuzzy collaborative intelligence approach, alpha-cut operations are applied to derive the fuzzy weights of criteria for each decision maker. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers. Subsequently, the fuzzy technique for order preference by similarity to the ideal solution is applied to assess the suitability of a smart technology application for fall detection. The fuzzy collaborative intelligence approach is a posterior-aggregation method that guarantees a consensus exists among decision makers. After applying the fuzzy collaborative intelligence approach to assess the suitabilities of four existing smart technology applications for fall detection, the most and least suitable smart technology applications were smart carpet and smart cane, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using three existing methods. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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12 pages, 261 KiB

Open AccessArticle

A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method

by Yun Jin, Hecheng Wu, Dechao Sun, Shouzhen Zeng, Dandan Luo and Bo Peng

Mathematics 2019, 7(10), 999; https://doi.org/10.3390/math7100999 - 21 Oct 2019

Cited by 16 | Viewed by 2272

Abstract

As a generalization of several fuzzy tools, picture fuzzy sets (PFSs) hold a special ability to perfectly portray inherent uncertain and vague decision preferences. The intention of this paper is to present a Pearson’s picture fuzzy correlation-based model for multi-attribute decision-making (MADM) analysis. [...] Read more.

As a generalization of several fuzzy tools, picture fuzzy sets (PFSs) hold a special ability to perfectly portray inherent uncertain and vague decision preferences. The intention of this paper is to present a Pearson’s picture fuzzy correlation-based model for multi-attribute decision-making (MADM) analysis. To this end, we develop a new correlation coefficient for picture fuzzy sets, based on which a Pearson’s picture fuzzy closeness index is introduced to simultaneously calculate the relative proximity to the positive ideal point and the relative distance from the negative ideal point. On the basis of the presented concepts, a Pearson’s correlation-based model is further presented to address picture fuzzy MADM problems. Finally, an illustrative example is provided to examine the usefulness and feasibility of the proposed methodology. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

17 pages, 523 KiB

Open AccessArticle

Fuzzy Multicriteria Decision-Making Model (MCDM) for Raw Materials Supplier Selection in Plastics Industry

by Chia-Nan Wang, Van Thanh Nguyen, Jiin-Tian Chyou, Tsung-Fu Lin and Tran Ngoc Nguyen

Mathematics 2019, 7(10), 981; https://doi.org/10.3390/math7100981 - 16 Oct 2019

Cited by 26 | Viewed by 3915

Abstract

To be able to compete in the domestic plastic industry, small and medium-sized enterprises producing plastic need to proactively find the supply of raw materials, avoiding shortages like in the previous years. Purchasing is extremely important and will create a competitive advantage with [...] Read more.

To be able to compete in the domestic plastic industry, small and medium-sized enterprises producing plastic need to proactively find the supply of raw materials, avoiding shortages like in the previous years. Purchasing is extremely important and will create a competitive advantage with competitors in the market, so finding suppliers will determine the success in the later stages of the production chain. With the development of the current information system, selection and evaluation have become important in order to achieve effective decision-making through optimal options. In this study, the authors provide a new approach for decision-makers in evaluating and selecting suppliers, which is formulated based on the supply chain operation reference (SCOR) model, fuzzy analytic network process (FANP), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The contribution of this research is to propose a multicriteria decision-making model (MCDM) for raw material supplier selection in the plastic industry. This research also provided a useful guideline for supplier selection in other industry. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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11 pages, 268 KiB

Open AccessArticle

Global Behavior of a Higher Order Fuzzy Difference Equation

by Guangwang Su, Taixiang Sun and Bin Qin

Mathematics 2019, 7(10), 938; https://doi.org/10.3390/math7100938 - 10 Oct 2019

Cited by 1 | Viewed by 1589

Abstract

Our aim in this paper is to investigate the convergence behavior of the positive solutions of a higher order fuzzy difference equation and show that all positive solutions of this equation converge to its unique positive equilibrium under appropriate assumptions. Furthermore, we give [...] Read more.

Our aim in this paper is to investigate the convergence behavior of the positive solutions of a higher order fuzzy difference equation and show that all positive solutions of this equation converge to its unique positive equilibrium under appropriate assumptions. Furthermore, we give two examples to account for the applicability of the main result of this paper. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

19 pages, 967 KiB

Open AccessArticle

Assessing the Performance of Green Mines via a Hesitant Fuzzy ORESTE–QUALIFLEX Method

by Weizhang Liang, Bing Dai, Guoyan Zhao and Hao Wu

Mathematics 2019, 7(9), 788; https://doi.org/10.3390/math7090788 - 27 Aug 2019

Cited by 29 | Viewed by 2527

Abstract

Due to various environmental issues caused by resource exploitation, establishing green mines is an essential measure to realize sustainable growth for mining companies. This research aimed to develop a novel methodology to evaluate the performance of green mines within hesitant fuzzy conditions. First, [...] Read more.

Due to various environmental issues caused by resource exploitation, establishing green mines is an essential measure to realize sustainable growth for mining companies. This research aimed to develop a novel methodology to evaluate the performance of green mines within hesitant fuzzy conditions. First, hesitant fuzzy sets (HFSs) were used to express original fuzzy assessment values. Then, the extended expert grading approach and the modified maximum deviation method with HFNs were combined to determine comprehensive importance degrees of criteria. Afterward, the traditional qualitative flexible (QUALIFLEX) method was integrated with the Organísation, rangement et synthèse de données relationnelles (ORESTE) model to achieve the rankings of mines. Finally, the proposed hesitant fuzzy ORESTE–QUALIFLEX approach was utilized to evaluate the performance of green mines. In addition, the robustness of the method was verified by a sensitivity analysis, while the effectiveness and strengths were certified by a comparison analysis. The results indicate that the proposed methodology has great robustness and advantages and that it is feasible and effective for the performance evaluation of green mines under hesitant fuzzy environment. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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22 pages, 916 KiB

Open AccessArticle

Linguistic Picture Fuzzy Dombi Aggregation Operators and Their Application in Multiple Attribute Group Decision Making Problem

by Muhammad Qiyas, Saleem Abdullah, Shahzaib Ashraf and Lazim Abdullah

Mathematics 2019, 7(8), 764; https://doi.org/10.3390/math7080764 - 20 Aug 2019

Cited by 24 | Viewed by 2861

Abstract

The aims of this study are to propose the linguistic picture fuzzy Dombi (LPFD) aggregation operators and decision-making approach to deal with uncertainties in the form of linguistic picture fuzzy sets. LPFD operators have more flexibility due to the general fuzzy set. Utilizing [...] Read more.

The aims of this study are to propose the linguistic picture fuzzy Dombi (LPFD) aggregation operators and decision-making approach to deal with uncertainties in the form of linguistic picture fuzzy sets. LPFD operators have more flexibility due to the general fuzzy set. Utilizing the Dombi operational rule, the series of Dombi aggregation operators were proposed, namely linguistic picture fuzzy Dombi arithmetic/geometric, ordered arithmetic/ordered geometric and Hybrid arithmetic/Hybrid geometric aggregation operators. The distinguished feature of these proposed operators is studied. At that point, we have used these Dombi operators to design a model to deal with multiple attribute decision-making (MADM) issues under linguistic picture fuzzy information. Finally, an illustrative example to evaluate the emerging technology enterprises is provided to demonstrate the effectiveness of the proposed approach, together with a sensitivity analysis and comparison analysis, proving that its results are feasible and credible. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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21 pages, 350 KiB

Open AccessArticle

A Novel Approach to Generalized Intuitionistic Fuzzy Soft Sets and Its Application in Decision Support System

by Muhammad Jabir Khan, Poom Kumam, Peide Liu, Wiyada Kumam and Shahzaib Ashraf

Mathematics 2019, 7(8), 742; https://doi.org/10.3390/math7080742 - 13 Aug 2019

Cited by 55 | Viewed by 4280

Abstract

The basic idea underneath the generalized intuitionistic fuzzy soft set is very constructive in decision making, since it considers how to exploit an extra intuitionistic fuzzy input from the director to make up for any distortion in the information provided by the evaluation [...] Read more.

The basic idea underneath the generalized intuitionistic fuzzy soft set is very constructive in decision making, since it considers how to exploit an extra intuitionistic fuzzy input from the director to make up for any distortion in the information provided by the evaluation experts, which is redefined and clarified by F. Feng. In this paper, we introduced a method to solve decision making problems using an adjustable weighted soft discernibility matrix in a generalized intuitionistic fuzzy soft set. We define the threshold functions like mid-threshold, top-bottom-threshold, bottom-bottom-threshold, top-top-threshold, med-threshold function and their level soft sets of the generalized intuitionistic fuzzy soft set. After, we proposed two algorithms based on threshold functions, a weighted soft discernibility matrix and a generalized intuitionistic fuzzy soft set and also to show the supremacy of the given methods we illustrate a descriptive example using a weighted soft discernibility matrix in the generalized intuitionistic fuzzy soft set. Results indicate that the proposed method is more effective and generalized over all existing methods of the fuzzy soft set. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

40 pages, 4688 KiB

Open AccessArticle

Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty

by Yan Sun and Xinya Li

Mathematics 2019, 7(8), 739; https://doi.org/10.3390/math7080739 - 12 Aug 2019

Cited by 22 | Viewed by 5588

Abstract

In this study, we systematically investigate a road-rail intermodal routing problem the optimization of which is oriented on the customer demands on transportation economy, timeliness and reliability. The road-rail intermodal transportation system is modelled as a hub-and-spoke network that contains time-flexible container truck [...] Read more.

In this study, we systematically investigate a road-rail intermodal routing problem the optimization of which is oriented on the customer demands on transportation economy, timeliness and reliability. The road-rail intermodal transportation system is modelled as a hub-and-spoke network that contains time-flexible container truck services and scheduled container train services. The transportation timeliness is optimized by using fuzzy soft time windows associated with the service level of the transportation. Reliability is enhanced by considering multiple sources of time uncertainty, including road travel time and loading/unloading time. Such uncertainty is modelled by using fuzzy set theory. Triangular fuzzy numbers are adopted to represent the uncertain time. Under the above consideration, we first establish a fuzzy mixed integer nonlinear programming model with a weighted objective that includes minimizing the costs and maximizing the service level for accomplishing transportation orders. Then we use the fuzzy expected value model and fuzzy chance-constrained programming separately to realize the defuzzification of the fuzzy objective and use fuzzy chance-constrained programming to deal with the fuzzy constraint. After defuzzification and linearization, an equivalent mixed integer linear programming (MILP) model is generated to enable the problem to be solved by mathematical programming software. Finally, a numerical case modified from our previous study is presented to demonstrate the feasibility of the proposed fuzzy programming approaches. Sensitivity analysis and fuzzy simulation are comprehensively utilized to discuss the effects of the fuzzy soft time windows and time uncertainty on the routing optimization and help decision makers to better design a crisp transportation plan that can effectively make tradeoffs among economy, timeliness and reliability. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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24 pages, 1110 KiB

Open AccessArticle

Child Development Influence Environmental Factors Determined Using Spherical Fuzzy Distance Measures

by Shahzaib Ashraf, Saleem Abdullah and Lazim Abdullah

Mathematics 2019, 7(8), 661; https://doi.org/10.3390/math7080661 - 24 Jul 2019

Cited by 64 | Viewed by 3660

Abstract

This paper aims to resolve the issue of the ranking of the fuzzy numbers in decision analysis, artificial intelligence, and optimization. In the literature, many ideas have been established for the ranking of the fuzzy numbers, and those ideas have some restrictions and [...] Read more.

This paper aims to resolve the issue of the ranking of the fuzzy numbers in decision analysis, artificial intelligence, and optimization. In the literature, many ideas have been established for the ranking of the fuzzy numbers, and those ideas have some restrictions and limitations. We propose a method based on spherical fuzzy numbers (SFNs) for ranking to overcome the existing restrictions. Further, we investigate the basic properties of SFNs, compare the idea of spherical fuzzy set with the picture fuzzy set, and establish some distance operators, namely spherical fuzzy distance-weighted averaging (SFDWA), spherical fuzzy distance order-weighted averaging (SFDOWA), and spherical fuzzy distance order-weighted average weighted averaging (SFDOWA WA) operators with the attribute weights’ information incompletely described. Further, we design an algorithm to solve decision analysis problems. Finally, to validate the usage and applicability of the established procedure, we assume the child development influence environmental factors problem as a practical application. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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105 pages, 873 KiB

Open AccessFeature PaperArticle

Solving Fuzzy Linear Programming Problems with Fuzzy Decision Variables

by Hsien-Chung Wu

Mathematics 2019, 7(7), 569; https://doi.org/10.3390/math7070569 - 26 Jun 2019

Cited by 5 | Viewed by 3820

Abstract

The numerical method for solving the fuzzy linear programming problems with fuzzy decision variables is proposed in this paper. The difficulty for solving this kind of problem is that the decision variables are assumed to be nonnegative fuzzy numbers instead of nonnegative real [...] Read more.

The numerical method for solving the fuzzy linear programming problems with fuzzy decision variables is proposed in this paper. The difficulty for solving this kind of problem is that the decision variables are assumed to be nonnegative fuzzy numbers instead of nonnegative real numbers. In other words, the decision variables are assumed to be membership functions. One of the purposes of this paper is to derive the analytic formula of error estimation regarding the approximate optimal solution. On the other hand, the existence of optimal solutions is also studied in this paper. Finally we present two numerical examples to demonstrate the usefulness of the numerical method. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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18 pages, 467 KiB

Open AccessArticle

A New Method to Optimize the Satisfaction Level of the Decision Maker in Fuzzy Geometric Programming Problems

by Armita Khorsandi, Bing-Yuan Cao and Hadi Nasseri

Mathematics 2019, 7(5), 464; https://doi.org/10.3390/math7050464 - 23 May 2019

Cited by 3 | Viewed by 2955

Abstract

Geometric programming problems are well-known in mathematical modeling. They are broadly used in diverse practical fields that are contemplated through an appropriate methodology. In this paper, a multi-parametric vector

α

is proposed for approaching the highest decision maker satisfaction. Hitherto, the simple parameter [...] Read more.

Geometric programming problems are well-known in mathematical modeling. They are broadly used in diverse practical fields that are contemplated through an appropriate methodology. In this paper, a multi-parametric vector

α

is proposed for approaching the highest decision maker satisfaction. Hitherto, the simple parameter

α

, which has a scalar role, has been considered in the problem. The parameter

α

is a vector whose range is within the region of the satisfaction area. Conventionally, it is assumed that the decision maker is sure about the parameters, but, in reality, it is mostly hesitant about them, so the parameters are presented in fuzzy numbers. In this method, the decision maker can attain different satisfaction levels in each constraint, and even full satisfaction can be reached in some constraints. The goal is to find the highest satisfaction degree to maintain an optimal solution. Moreover, the objective function is turned into a constraint, i.e., one more dimension is added to n-dimensional multi-parametric

α

. Thus, the fuzzy geometric programming problem under this multi-parametric vector

α \in {(0, 1]}^{n + 1}

gives a maximum satisfaction level to the decision maker. A numerical example is presented to illustrate the proposed method and the superiority of this multi-parametric

α

over the simple one. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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15 pages, 2824 KiB

Open AccessArticle

Fuzzy Multi-Criteria Decision-Making Model for Supplier Evaluation and Selection in a Wind Power Plant Project

by Chia-Nan Wang, Ching-Yu Yang and Hung-Chun Cheng

Mathematics 2019, 7(5), 417; https://doi.org/10.3390/math7050417 - 10 May 2019

Cited by 26 | Viewed by 4619

Abstract

In order to meet ambitious growth targets in the medium term, Vietnam must continue exploiting traditional energy sources. In the longer term, Vietnam has to develop a strategy and roadmap for the development of new energy sources. In these new energy sources, wind [...] Read more.

In order to meet ambitious growth targets in the medium term, Vietnam must continue exploiting traditional energy sources. In the longer term, Vietnam has to develop a strategy and roadmap for the development of new energy sources. In these new energy sources, wind energy has emerged as a viable option. Given the geographic conditions of a locality with a long coastline and high winds that are fairly distributed all year, many wind-power plants are being built in Vietnam. One of the most important pieces of equipment in a wind-power plant is the wind turbine. The wind turbine suppliers’ selection is a complex and multicriteria decision-making (MCDM) process that can reduce the costs of procuring equipment and aid in receiving products on time. Many studies have applied the MCDM model to various fields of science and engineering. One of the fields that the MCDM approaches have been applied to is the supplier selection problem. Supplier selection is an important issue of the MCDM model. Especially in a renewable energy project, decision-makers have to evaluate both natural and society factors. Although some researchers have reviewed the applications of the MCDM model in wind turbine supplier selection, limited work has focused on this problem in a fuzzy environment. Therefore, in this work, the authors propose a fuzzy MCDM model for the wind turbine supplier selection process under fuzzy environment conditions. In the first step, all factors for wind turbine supplier selection are identified by supply chain operations reference (SCOR) metrics and the results from a review of the literature. A fuzzy analytic network process (FANP) model is applied for determining the weight of all the criteria in the second stage, and the technique for order preference by similarity to an ideal solution (TOPSIS) model is used to rank all the potential suppliers in the final stage. As a result, Decision-Making Unit 010 (DMU010) becomes an optimal option for the wind turbine supplier selection processes. The contribution of this research is to develop new hybrid fuzzy MCDM approaches for wind turbine supplier selections. Furthermore, this work presents useful guidelines for wind turbines as well as provides a guideline for supplier selection in other industries. Full article

(This article belongs to the Special Issue Operations Research Using Fuzzy Sets Theory)

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