Axioms

Research

16 pages, 1367 KiB

Open AccessArticle

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

by Martha Ramírez, Patricia Melin and Oscar Castillo

Axioms 2024, 13(6), 368; https://doi.org/10.3390/axioms13060368 - 30 May 2024

Viewed by 167

Abstract

Economic risk is a probability that measures the possible alterations, as well as the uncertainty, generated by multiple internal or external factors. Sometimes it could cause the impossibility of guaranteeing the level of compliance with organizational goals and objectives, which is why for [...] Read more.

Economic risk is a probability that measures the possible alterations, as well as the uncertainty, generated by multiple internal or external factors. Sometimes it could cause the impossibility of guaranteeing the level of compliance with organizational goals and objectives, which is why for their administration they are frequently divided into multiple categories according to their consequences and impact. Global indicators are dynamic and sometimes the correlation is uncertain because they depend largely on a combination of economic, social, and environmental factors. Thus, our proposal consists of a model for prediction and classification of multivariate risk factors such as birth rate and population growth, among others, using multiple neural networks and General Type-2 fuzzy systems. The contribution is the proposal to integrate multiple variables of several time series using both supervised and unsupervised neural networks, and a generalized Type-2 fuzzy integration. Results show the advantages of utilizing the method for the fuzzy integration of multiple time series attributes, with which the user can then prevent future threats from the global environment that impact the scheduled compliance process. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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17 pages, 327 KiB

Open AccessArticle

An Intuitionistic Fuzzy Multi-Criteria Approach for Prioritizing Failures That Cause Overproduction: A Case Study in Process Manufacturing

by Ranka Sudžum, Snežana Nestić, Nikola Komatina and Milija Kraišnik

Axioms 2024, 13(6), 357; https://doi.org/10.3390/axioms13060357 - 27 May 2024

Viewed by 198

Abstract

Overproduction is one of the most significant wastes of Lean that can occur in any manufacturing company. Identifying and prioritizing failures that lead to overproduction are crucial tasks for operational managers and engineers. Therefore, this paper presents a new approach for determining the [...] Read more.

Overproduction is one of the most significant wastes of Lean that can occur in any manufacturing company. Identifying and prioritizing failures that lead to overproduction are crucial tasks for operational managers and engineers. Therefore, this paper presents a new approach for determining the priority of failures that cause overproduction, based on an intuitionistic fuzzy Multi-Criteria Optimization model and the Failure Mode and Effects Analysis framework. The existing vagueness in the relative importance of risk factors and their values is described using natural language words, which are modeled with trapezoidal intuitionistic fuzzy numbers. Determining the relative importance of risk factors is defined as a fuzzy group decision-making problem, and the weight vector is obtained by applying the proposed Analytical Hierarchy Process with trapezoidal intuitionistic fuzzy numbers. The compromise solution, as well as the stability check of the obtained compromise solution, is achieved using the proposed Multi-Criteria Optimization and Compromise Solution with trapezoidal intuitionistic fuzzy numbers. The proposed model was applied to data collected from a process manufacturing company. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

26 pages, 343 KiB

Open AccessArticle

Using Genetic Algorithms and Core Values of Cooperative Games to Solve Fuzzy Multiobjective Optimization Problems

by Hsien-Chung Wu

Axioms 2024, 13(5), 298; https://doi.org/10.3390/axioms13050298 - 29 Apr 2024

Viewed by 480

Abstract

A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is [...] Read more.

A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mechanical procedure to avoid this subjective biases. We are going to formulate a cooperative game using the

α

-level functions of the multiple fuzzy objective functions. Under this setting, the suitable coefficients can be determined mechanically by involving the core values of the cooperative game, which is formulated using the multiple fuzzy objective functions. We shall prove that the optimal solutions of the transformed scalar optimization problem are indeed the nondominated solutions of fuzzy multiobjective optimization problem. Since the core-nondominated solutions will depend on the coefficients that are determined by the core values of cooperative game, there will be a lot of core-nondominated solutions that will also depend on the corresponding coefficients. In order to obtain the best core-nondominated solution, we shall invoke the genetic algorithms by evolving the coefficients. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

18 pages, 1346 KiB

Open AccessArticle

Probabilistic Interval Ordering Prioritized Averaging Operator and Its Application in Bank Investment Decision Making

by Chuanyang Ruan, Shicheng Gong and Xiangjing Chen

Axioms 2023, 12(11), 1007; https://doi.org/10.3390/axioms12111007 - 26 Oct 2023

Viewed by 864

Abstract

Probabilistic interval ordering, as a helpful tool for expressing positive and negative information, can effectively address multi-attribute decision-making (MADM) problems in reality. However, when dealing with a significant number of decision-makers and decision attributes, the priority relationships between different attributes and their relative [...] Read more.

Probabilistic interval ordering, as a helpful tool for expressing positive and negative information, can effectively address multi-attribute decision-making (MADM) problems in reality. However, when dealing with a significant number of decision-makers and decision attributes, the priority relationships between different attributes and their relative importance are often neglected, resulting in deviations in decision outcomes. Therefore, this paper combines probability interval ordering, the prioritized aggregation (PA) operator, and the Gauss–Legendre algorithm to address the MADM problem with prioritized attributes. First, considering the significance of interval priority ordering and the distribution characteristics of attribute priority, the paper introduces probability interval ordering elements that incorporate attribute priority, and it proposes the probabilistic interval ordering prioritized averaging (PIOPA) operator. Then, the probabilistic interval ordering Gauss–Legendre prioritized averaging operator (PIOGPA) is defined based on the Gauss–Legendre algorithm, and various excellent properties of this operator are explored. This operator considers the priority relationships between attributes and their importance level, making it more capable of handling uncertainty. Finally, a new MADM method is constructed based on the PIOGPA operator using probability intervals and employs the arithmetic–geometric mean (AGM) algorithm to compute the weight of each attribute. The feasibility and soundness of the proposed method are confirmed through a numerical example and comparative analysis. The MADM method introduced in this paper assigns higher weights to higher-priority attributes to establish fixed attribute weights, and it reduces the impact of other attributes on decision-making results. It also utilizes the Gauss AGM algorithm to streamline the computational complexity and enhance the decision-making effectiveness. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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23 pages, 2036 KiB

Open AccessArticle

A Supplier Selection Decision-Making Approach for Complex Product Development Based on Hesitant Fuzzy Information

by Baodong Li, Jiafu Su, Boqiao Yuan, Lvcheng Li, Yihuan Zhao, Zhidan Qin and Li Qian

Axioms 2023, 12(11), 1006; https://doi.org/10.3390/axioms12111006 - 25 Oct 2023

Viewed by 1050

Abstract

During the development process of complex products, selecting the best desirable alternative supplier is a challenge since an improperly selected alternative may cause losing capacity and increasing the cycle time and cost of development for a company. For this multiple-attribute decision-making problem of [...] Read more.

During the development process of complex products, selecting the best desirable alternative supplier is a challenge since an improperly selected alternative may cause losing capacity and increasing the cycle time and cost of development for a company. For this multiple-attribute decision-making problem of supplier selection, in this paper, a supplier selection problem in which the decision data are hesitant fuzzy information and the attribute weight is unknown in complex product development is investigated, and a supplier selection decision-making approach based on hesitant fuzzy information is proposed. Firstly, a bidirectional projection based on hesitant fuzzy information is established, and then the measurement equation for the degree of closeness is improved. Further, an attribute weight determination model which minimizes the projection total deviation for the hesitant fuzzy elements is constructed. By solving this model, scientific and reasonable attribute weights are provided. Subsequently, an illustrative example is employed to not only give the ranking result of alternative suppliers but also demonstrate the validity and feasibility of the developed approach. Meanwhile, sensitivity analysis and comparative analysis are put forward to illustrate the stability of the given final ranking result and the advantages and reliability of the constructed method. For alternative or strategy selection, this proposed approach can be used as a decision-making means when uncertainties are inherent. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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21 pages, 1564 KiB

Open AccessArticle

Enhancing Contractor Selection Process by a New Interval-Valued Fuzzy Decision-Making Model Based on SWARA and CoCoSo Methods

by Sajjad Karami, Seyed Meysam Mousavi and Jurgita Antucheviciene

Axioms 2023, 12(8), 729; https://doi.org/10.3390/axioms12080729 - 27 Jul 2023

Cited by 3 | Viewed by 933

Abstract

Contractor selection is a crucial aspect of construction projects, with a significant impact on project success. However, traditional methods may not effectively handle the complexities and uncertainties involved in decision-making. To address this, advanced techniques like Multi-Criteria Decision-Making (MCDM) have been developed. In [...] Read more.

Contractor selection is a crucial aspect of construction projects, with a significant impact on project success. However, traditional methods may not effectively handle the complexities and uncertainties involved in decision-making. To address this, advanced techniques like Multi-Criteria Decision-Making (MCDM) have been developed. In this study, we propose a new approach based on two uncertain methods, Interval-Valued Fuzzy Step-Wise Weight Assessment Ratio Analysis (IVF-SWARA) and Interval-Valued Fuzzy Combined Compromise Solution (IVF-CoCoSo), for contractor selection in construction projects. These methods use interval-valued fuzzy numbers (IVFNs) to handle decision-making under uncertainty and imprecision. By leveraging the benefits of IVFNs, the proposed methods enhance accuracy and flexibility, enabling more informed and reliable decisions. An application example illustrates the effectiveness of the methods, and sensitivity analysis examines how varying criteria weights affect contractor rankings. The study concludes that the IVF-SWARA and IVF-CoCoSo methods assist decision-makers in selecting suitable contractors and achieving project success. These methods provide a robust framework to navigate complexities and uncertainties, leading to improved decision-making in contractor selection for construction projects. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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16 pages, 583 KiB

Open AccessArticle

Selection of a Forklift for a Cargo Company with Fuzzy BWM and Fuzzy MCRAT Methods

by Alptekin Ulutaş, Ayse Topal, Darjan Karabasevic and Figen Balo

Axioms 2023, 12(5), 467; https://doi.org/10.3390/axioms12050467 - 12 May 2023

Cited by 4 | Viewed by 1236

Abstract

Material handling is a cost-intensive operation for businesses. There are several alternative types of equipment for material handling, therefore it is important to select the best one among them to decrease the cost. As there are several different alternatives and criteria which are [...] Read more.

Material handling is a cost-intensive operation for businesses. There are several alternative types of equipment for material handling, therefore it is important to select the best one among them to decrease the cost. As there are several different alternatives and criteria which are used to assess these alternatives, multi-criteria decision making (MCDM) techniques are useful to determine the optimal material handling equipment (MHE) for businesses. In this study, fuzzy BWM for determining weights of criteria and the fuzzy Multiple Criteria Ranking by Alternative Trace (MCRAT) method have been used for ranking forklift alternatives. This study’s significance in the literature will be the creation of a novel fuzzy MCDM technique with the application of fuzzy MCRAT. Furthermore, there are relatively few studies employing the MCRAT approach in the literature; therefore, this study will provide additional data and outcomes from this method to the literature. The findings present that the forklift with the code FLT-3 performed the best, whereas the forklift with the code FLT-2 had the worst performance, according to the fuzzy MCRAT technique. According to the comparison analysis, the fuzzy MCRAT produced the same results as the fuzzy ARAS and had a few subtle differences to fuzzy MARCOS. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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14 pages, 1464 KiB

Open AccessArticle

Statistical Fuzzy Reliability Assessment of a Blended System

by Aayushi Chachra, Akshay Kumar, Mangey Ram and Ioannis S. Triantafyllou

Axioms 2023, 12(5), 419; https://doi.org/10.3390/axioms12050419 - 25 Apr 2023

Cited by 3 | Viewed by 1284

Abstract

Fuzzy sets have been proven to constitute an asset in the evolution of reliability theory in recent decades. Their contribution in addressing the possibility of errors, insufficiency of data, randomness, or fuzziness, either in the system or in the accumulation of any data [...] Read more.

Fuzzy sets have been proven to constitute an asset in the evolution of reliability theory in recent decades. Their contribution in addressing the possibility of errors, insufficiency of data, randomness, or fuzziness, either in the system or in the accumulation of any data for the respective system, which is overlooked in the traditional reliability assessment, seems to be quite crucial. The present work deals with the statistical fuzzy reliability evaluation of a blended system that comprises two subsystems. One system contains two components aligned in a parallel configuration, and the other is a 3-out-of-5 system. The reliability of this model is assessed using two approaches to intuitionistic fuzzy sets (IFS), namely, traditional IFS and interval-valued intuitionistic fuzzy sets (IVIFS). Three cases are considered in each approach, which are compared individually as well as with each other. It was established that the IVIFS yield better results than the IFS. The obtained results are displayed in both tabular and graphical forms for better assessment. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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19 pages, 928 KiB

Open AccessArticle

Properties of Convex Fuzzy-Number-Valued Functions on Harmonic Convex Set in the Second Sense and Related Inequalities via Up and Down Fuzzy Relation

by Muhammad Bilal Khan, Željko Stević, Abdulwadoud A. Maash, Muhammad Aslam Noor and Mohamed S. Soliman

Axioms 2023, 12(4), 399; https://doi.org/10.3390/axioms12040399 - 20 Apr 2023

Viewed by 940

Abstract

In this paper, we provide different variants of the Hermite–Hadamard (

H \cdot H

) inequality using the concept of a new class of convex mappings, which is referred to as up and down harmonically

s

-convex fuzzy-number-valued functions (

U D H

[...] Read more.

In this paper, we provide different variants of the Hermite–Hadamard (

H \cdot H

) inequality using the concept of a new class of convex mappings, which is referred to as up and down harmonically

s

-convex fuzzy-number-valued functions (

U D H

s

-convex

F N V M

) in the second sense based on the up and down fuzzy inclusion relation. The findings are confirmed with certain numerical calculations that take a few appropriate examples into account. The results deal with various integrals of the

\frac{2 ρ σ}{ρ + σ}

type and are innovative in the setting of up and down harmonically

s

-convex fuzzy-number-valued functions. Moreover, we acquire classical and new exceptional cases that can be seen as applications of our main outcomes. In our opinion, this will make a significant contribution to encouraging more research. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Fuzzy Modeling, Optimization and Computational Intelligence)

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