Assessment of national innovation capabilities of OECD countries using trapezoidal interval type-2 fuzzy ELECTRE III method

2020 ◽  
Author(s):  
Geetha Selvaraj ◽  
Jeonghwan Jeon
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Research Work ◽  
Oecd Countries ◽  
Content Type ◽  
Electre Iii ◽  
Standard Values ◽  
Interval Type ◽  
Credibility Degree

PurposeFor a nation to become a superpower, it's scientific and technological advancement is essential. Each country is exploring how to improve themselves in terms of science and technology. The authors analyzed the innovation capabilities of 35 OECD countries that have not recently joined Lithuania.Design/methodology/approachIn recent years, a lot of research work has been done on trapezoidal interval type-2 fuzzy sets (TIT-2 FS), and many research works have been published. The trapezoidal interval type-2 fuzzy set helps effectively to represent the uncertainty comparatively than the type-1 fuzzy set. Taking advantage of this effectiveness, the authors extend the best multi-criteria decision making method (MCDM) for trapezoidal interval type-2 fuzzy sets. Here, ELimination and Choice Expressing REality III (ELECTRE III) method in the trapezoidal interval type-2 fuzzy set environment is proposed.FindingsThis analysis helps to the OECD countries to develop their level of innovation in the criteria. The authors are making this evaluation for the year 2018 based on the 31 criteria. Application of the proposed method expressed by evaluation of the national innovation capability problem. Based on the obtained results, the top five countries are United States, Switzerland, Canada, Germany and Japan.Originality/valueThe authors collected required data from different available data sources like OECD, IMD, USPTO, ITU and surveyed data reported by KISTEP. After collecting all the data from different sources, the authors calculated the standard values as KISTEP. After converting the standard values into trapezoidal interval type-2 fuzzy values, the authors construct a decision matrix based on these values. Then, the authors determined the possibility mean values and preference. Then, they calculated the concordance and discordance credibility degree values. Finally, they ranked OECD countries by the net credibility degree. The results are computed by using the MATLAB software.

Risk assessment for the industrial network based on interval type-2 fuzzy sets

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 916-937
Author(s):  
Chao Ren ◽  
Xiaoxing Liu ◽  
Zongqing Zhang
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Risk Evaluation ◽  
Quantitative Risk Assessment ◽  
Content Type ◽  
Industrial Network ◽  
Risk Magnitude ◽  
Interval Type

Purpose The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment. Design/methodology/approach This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters. Findings The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method. Research limitations/implications There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study. Originality/value The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

An interval type-2 fuzzy ANP approach to evaluate enterprise technological innovation ability

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1486-1500 ◽  
Author(s):  
Tong Wu ◽  
Xinwang Liu
Keyword(s):  
Fuzzy Sets ◽  
Evaluation Model ◽  
Influence Factors ◽  
Content Type ◽  
Fuzzy Anp ◽  
Enterprise Technology ◽  
Innovation Ability ◽  
Interval Type

Purpose The purpose of this paper is to overcome the drawbacks of analytic hierarchy process in solving complex decision-making problems, especially for the evaluation of enterprise technology innovation ability (ETIA). Because interval type-2 fuzzy sets (IT2 FSs) can handle uncertainty linguistic variables in a more flexible and precise way than type-1 fuzzy sets with their second fuzzy membership functions, a fuzzy ANP method with IT2 FSs is proposed to evaluate the ETIA. Design/methodology/approach The criteria of evaluation on ETIA are identified and an evaluation model for ETIA is constructed on the basis of the application analysis of ETIA and theoretical design of ANP. In addition, two different ranking methods of IT2 FSs are applied in processing the relationships between influence factors of ETIA. Findings By using the proposed interval type-2 fuzzy ANP (IT2 FANP) method, the efficiencies of the whole evaluation of ETIA can be measured and the important factors in the ETIA can also be determined. Compared with the type-1 FANP through the ranking results, the proposed IT2 FANP is more reasonable and robust for the evaluation of ETIA. Practical implications The proposed IT2 FANP method is applied on the evaluation of ETIA. With respect to the application, the proposed method can be used to evaluate many more complex problems that contain feedback and circular relationships. Originality/value The proposed IT2 FANP approach can solve the complexities and uncertainties at the same time. Considering the subjective initiative of decision-makers and the feedback between influence factors, the proposed method is more efficient than the existing type-1 approaches in the literature.

A hybrid approach for selecting sustainable suppliers and determining order allocation based on interval type-2 fuzzy sets

2020 ◽  
Vol 33 (5) ◽  
pp. 923-945
Author(s):  
Ahmet Çalık
Keyword(s):  
Fuzzy Sets ◽  
Hybrid Approach ◽  
Agricultural Machinery ◽  
Order Allocation ◽  
Economic Criterion ◽  
Content Type ◽  
Machinery Industry ◽  
Sustainable Supplier Selection ◽  
Interval Type

PurposeThis study aims to create a model for defining the best supplier for a company and allocating order that considers sustainability criteria beyond the traditional selection criteria.Design/methodology/approachIn this paper, sustainable supplier Selection and order allocation (SSS and OA) problem is managed based on a multiobjective linear programming (MOLP) model that incorporates sustainability dimensions. First, an interval type-2 fuzzy analytic hierarchy process (FAHP) method is applied for the main criteria and subcriteria to determine the weight of the selected criteria. Then, these values are used to convert the proposed MOLP model into a single-objective model.FindingsThe economic criterion (0.438) was the most important criterion for SSS in the agricultural machinery sector, followed by the social criterion (0.333) and the environmental criterion (0.229).Practical implicationsThe results show that the proposed framework can be utilized by the agricultural machinery industry for SSS and OA.Originality/valueThe proposed framework provides to develop an integrated model by interval type-2 fuzzy sets for SSS and OA, taking into account the relationships between qualitative and quantitative evaluation criteria with different priorities. The validity of the developed model is confirmed by a case study of the agricultural machinery industry in Turkey.

A Comparative Investigation of Type-2 Fuzzy Sets, Nonstationary Fuzzy Sets and Cloud Models

2016 ◽  
Vol 24 (02) ◽  
pp. 213-227 ◽  
Author(s):  
Han-Chen Huang ◽  
Xiaojun Yang
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Cloud Model ◽  
Comparative Investigation ◽  
Mathematical Methods ◽  
Moderate Amount ◽  
Cloud Models ◽  
Interval Type

Since Zadeh introduced fuzzy sets, a lot of extensions of this concept have been proposed, such as type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models, to represent higher levels of uncertainty. This paper provides a comparative investigation of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Type-2 fuzzy sets study the fuzziness of the membership function (MF) using primary MF and secondary MF based on analytic mathematical methods; nonstationary fuzzy sets study the randomness of the MF using primary MF and variation function based on type-1 fuzzy sets theory; cloud models study the randomness of the distribution of samples in the universe and generate random membership grades (MGs) using two random variables based on probability and statistic mathematical methods. They all concentrate on dealing with the uncertainty of the MF or the MG which type-1 fuzzy sets do not consider, and thus have many similarities. Moreover, we find out that, the same qualitative concept “moderate amount” can be represented by an interval type-2 fuzzy set, a nonstationary fuzzy set or a normal cloud model, respectively. Then, we propose a unified mathematical expression for the interval type-2 fuzzy set, nonstationary fuzzy set and normal cloud model. On the other hand, we also find out that, the theory fundament and underlying motivations of these models are quite different. Therefore, We summarize detailed comparisons of distinctive properties of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Further, we study their diverse characteristics of distributions of MGs across vertical slices. The comparative investigation shows that these models are complementary to describe the uncertainty from different points of view. Thus, this paper provides a fundamental contribution and makes a basic reference for knowledge representation and other applications with uncertainty.

A GIS-based interval type-2 fuzzy set for public bread factory site selection

2018 ◽  
Vol 31 (6) ◽  
pp. 820-847 ◽  
Author(s):  
Muhammet Deveci ◽  
Ibrahim Zeki Akyurt ◽  
Selahattin Yavuz
Keyword(s):  
Fuzzy Set ◽  
Site Selection ◽  
Two Stage ◽  
Content Type ◽  
Location Selection ◽  
The Public ◽  
Second Stage ◽  
Interval Type ◽  
Factory Site

Purpose The purpose of this paper is to present a new public bread factory location selection for Istanbul Metropolitan Municipality (IMM). Design/methodology/approach A two-stage methodology is proposed to determine the location for the public bread factory facility. This framework is based on both geographic information systems (GIS) and multi-criteria decision-making (MCDM) techniques. The first stage of the methodology aims to decrease the number of possible alternative locations to simplify the selection activity by applying GIS; the second stage utilises interval type-2 fuzzy MCDM approach to exactly determine the public bread factory site location. Findings In this study, the authors present weighted normalised-based interval type-2 hesitant fuzzy and interval type-2 hesitant fuzzy sets (IT2HFSs)-based compressed proportional assessment (COPRAS) methods to overcome facility location selection problem for a fourth public bread factory in Istanbul. Practical implications The results show that the proposed approach is practical and can be employed by the bakery industry. Originality/value In this study, the authors present a two-stage methodology for public bread factory site selection. In the first stage, the number of alternatives is reduced by the GIS. In the second stage, an interval type-2 fuzzy set is implemented for the evaluation of public bakery factory site alternatives. A new integrated approach based on COPRAS method and weighted normalised with IT2HFSs is proposed.

A multi-level programming model for green supplier selection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Srikant Gupta ◽  
Prasenjit Chatterjee ◽  
Morteza Yazdani ◽  
Ernesto D.R. Santibanez Gonzalez
Keyword(s):  
Fuzzy Sets ◽  
Large Scale ◽  
Social Issues ◽  
Programming Model ◽  
Global Market ◽  
Content Type ◽  
Unit Energy ◽  
Proposed Model ◽  
Interval Type

PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

Design of single and double acceptance sampling plans based on interval type-2 fuzzy sets

2021 ◽  
pp. 1-13
Author(s):  
Gürkan Işık ◽  
İhsan Kaya
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Measurement Errors ◽  
Uncertainty Modeling ◽  
Characteristic Functions ◽  
Acceptance Sampling ◽  
Sampling Plans ◽  
Acceptance Sampling Plans ◽  
Interval Type

Defectiveness of items is generally considered as a certain value in acceptance sampling plans (ASPs). It is clear that, it may not be certainly known in some real-case problems. Uncertainties of the inspection process such as measurement errors, inspectors’ hesitancies or vagueness of the process etc. should be taken into account to obtain more reliable results. The fuzzy set theory (FST) is one of the best methods to overcome these problems. There are some studies in the literature formulating the ASPs with the help of FST. Deciding the right membership functions of the fuzzy sets (FSs) has a vital importance on the quality of the uncertainty modeling. Additionally, the fuzzy set extensions have been offered to model more complicated uncertainties to achieve better modeling. As one of these extensions, type-2 fuzzy sets (T2FSs) gives an ability to model uncertainty in situations where it is not possible to determine exact membership function parameters. In this study, single and double ASPs based on interval T2FSs (IT2FSs) have been designed for binomial and Poisson distributions. Thus, it becomes possible to make more flexible, sensitive and descriptive sensitivity analyzes. The main characteristic functions of ASPs have been derived and the suggested formulations have been illustrated on a comparative application from manufacturing process. Results allowing for more comprehensive analysis as against to the traditional and T1FSs based plans have been obtained.

scholarly journals An integrated group FWA-ELECTRE III approach based on interval type-2 fuzzy sets for solving the MCDM problems using limit distance mean

2020 ◽  
Vol 6 (2) ◽  
pp. 355-389 ◽  
Author(s):  
A. Mohamadghasemi ◽  
A. Hadi-Vencheh ◽  
F. Hosseinzadeh Lotfi ◽  
M. Khalilzadeh
Keyword(s):  
Fuzzy Sets ◽  
Electre Iii ◽  
Interval Type

An Entropy of Interval Type-2 Fuzzy Sets

2013 ◽  
Vol 321-324 ◽  
pp. 1999-2003 ◽  
Author(s):  
Gao Zheng ◽  
Shi Wei Yin
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Entropy ◽  
Calculation Formula ◽  
Membership Functions ◽  
Axiomatic Definition ◽  
Fuzzy Degree ◽  
Interval Type

The entropy shows the fuzzy degree of a fuzzy set (FS) and can be used in various areas. Aiming at the characteristics of the fuzzy entropy and type-2 fuzzy sets (IT2 FSs), we introduce a new entropy of IT2 FSs in this paper. At first, we select an axiomatic definition for it. Then, considering a fact that the operations of IT2 FSs depend on the upper membership functions (UMFs) and lower membership functions (LMFs), we propose a calculation formula and verify it accords with the four axioms of the selected definition. Finally, we use an example to illuminate its reasonable performance.

Different methods to fuzzy X¯-R control charts used in production

2018 ◽  
Vol 31 (6) ◽  
pp. 848-866 ◽  
Author(s):  
Hatice Ercan Teksen ◽  
Ahmet Sermet Anagun
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Quality Characteristic ◽  
Content Type ◽  
Fuzzy Methods ◽  
Interval Type ◽  
Control Limits

PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits ofX¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to theX¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used inX¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to theX¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods onX¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered forX¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such asX¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets onX¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

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