Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

Zhu, G.; Ma, Jin; Hu, Jie

doi:10.1002/int.22541

Cited by 16 publications

(6 citation statements)

References 60 publications

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“…In this case, few scholars combined MAIRCA with fuzzy set theory to resolve decision-making issues. For instances, Chatterjee et al [ 30 ] Evaluated the performance of suppliers with a rough MAIRCA technique for green supply chain implementation in electronics industry; Adar and Kilic Delice [ 31 ] modified the MAIRCA model with hesitant fuzzy linguistic term sets; Boral et al [ 32 ] integrated AHP with MAIRCA in a fuzzy environment for fuzzy failure modes and effects analysis; Bagheri et al [ 33 ] identified the uncertainty factors on the constructional project with an extended MAIRCA under hesitant fuzzy circumstances; García Mestanza and Bakhat [ 34 ] presented a fuzzy MAIRCA for evaluating over-tourism; Zhu et al [ 35 ] established an extended MAIRCA model to assess biological inspiration for biologically inspired design in fuzzy rough environments; Ecer [ 36 ] modified the MAIRCA with intuitionistic fuzzy numbers to choose coronavirus vaccine in the age of COVID-19. However, the MAIRCA has neither been extended in a picture fuzzy environment, nor been utilized to deal with teaching quality evaluation issues.…”

Section: Introductionmentioning

confidence: 99%

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Chen

Luo

2023

PLoS ONE

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The teaching quality evaluation of physical education is an important measure to promote the professional development of physical teachers, improve the quality of school teaching and personnel training. It is helpful for students to achieve all-round development and better meet the needs of modern talents in the new era. This study aims to establish a novel MCDM (multi-criteria decision-making) framework for evaluating teaching quality of physical education. First, PFNs (picture fuzzy numbers) are suggested to reflect dissimilar attitudes or preferences of decision makers. Then, the typical SWARA (Step-wise Weight Assessment Ratio Analysis) model is modified with PFNs to calculate the weights of evaluation criteria. Considering that some criteria are non-compensatory during the evaluation process, the idea of ELECTRE (elimination and choice translating reality) is introduced to obtain the ranking results of alternatives. Specially, the MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method is extended to construct the difference matrix in a picture fuzzy environment. Last, the hybrid MCDM model is utilized to assess the teaching quality of physical education. Its superiority is justified through comparison analyses. Results show that our approach is practicable and can provide instructions for the teaching quality evaluation of physical education.

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Section: Introductionmentioning

confidence: 99%

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Chen

Luo

2023

PLoS ONE

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“…and Lim C π z (Luo et al 2021). Equations ( 36)-( 39) explain the ways to obtain a total relation matrix (TRM) (Zhu et al 2021). Additionally, the defuzzification process is done by Eqs.…”

Section: Dematel Based On Pfrssmentioning

confidence: 99%

Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers

Yüksel

Dınçer

2022

Financ Innov

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This study evaluates financial innovation priorities for renewable energy investors by generating a novel hybrid fuzzy decision-making model. First, SERVQUAL-based customer needs for financial innovation are weighted with decision-making trial and evaluation laboratory based on picture fuzzy sets. Second, the financial innovation priorities are ranked by technique for order preference by similarity to ideal solutions based on picture fuzzy rough sets. In this process, Theory of the solution of inventive problems-based technical characteristics for financial services, the process for innovative services, and competencies for financial innovation are considered using quality function deployment phases. In addition, the Vise Kriterijumska Optimizacija I Kompromisno Resenje method is also considered for an alternative ranking. Similarly, sensitivity analysis is also performed by considering five different cases. It is determined that the ranking priorities based on the proposed model are almost identical, demonstrating the proposed model’s validity and reliability. Assurance is the most crucial factor for the customer needs regarding the financial innovation priorities for renewable energy investors. Concerning the financial innovation priorities, the product is the essential priority for financial innovation; hence, it is recommended that companies engage qualified employees to effectively design the financial innovation for renewable energy investors. Additionally, necessary training should be given to the employees who currently work in the company, which can increase the renewable energy investors’ trust in the innovative financial products. Companies should mainly focus on the product to provide better financial innovation to attract renewable energy investors. An effectively designed financial innovation product can help solve the financing problem of renewable energy investors.

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“…The merits of the MAIRCA method are described as below: (1) it can be employed to settle decision-making issues which have multitudinous criteria and alternatives; (2) it can also solve decision problems with mixed quantitative and qualitative evaluation criteria; (3) the decision process of MAIRCA is easily understood and can be flexibly applied in combination with other methods; and (4) the method has a distinctive linear normalization algorithm which can obtain highly reliable discrepancies and produce consistent results. Due to the above merits, many scholars have employed the MAIRCA to settle real-world decision issues in a great number of fields, such as flood susceptibility assessment (Hadian et al, 2022), ammunition depot site selection problems (Gigovic et al, 2016), biological inspiration evaluation (Zhu et al, 2021), supplier performance evaluation (Chatterjee et al, 2018), failure risk evaluation (Boral et al, 2020), business partner selection (Trung et al, 2022), and energy storage technology selection . To indicate the ambiguous and indetermined evaluation information, some scholars have extended the traditional MAIRCA method by combining it into various decision environments, such as classical fuzzy sets (Gul and Ak, 2020;Boral et al, 2020;Mestanza and Bakhat, 2021), fuzzy rough sets (Zhu et al, 2021), spherical fuzzy sets (Trung et al, 2022;Erdogan, 2022), rough sets (Chatterjee et al, 2018;Pamucar et al, 2017a;Bozanic et al, 2020;Pamucar et al, 2017b) and intuitionistic fuzzy sets (Ecer, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the above merits, many scholars have employed the MAIRCA to settle real-world decision issues in a great number of fields, such as flood susceptibility assessment (Hadian et al, 2022), ammunition depot site selection problems (Gigovic et al, 2016), biological inspiration evaluation (Zhu et al, 2021), supplier performance evaluation (Chatterjee et al, 2018), failure risk evaluation (Boral et al, 2020), business partner selection (Trung et al, 2022), and energy storage technology selection . To indicate the ambiguous and indetermined evaluation information, some scholars have extended the traditional MAIRCA method by combining it into various decision environments, such as classical fuzzy sets (Gul and Ak, 2020;Boral et al, 2020;Mestanza and Bakhat, 2021), fuzzy rough sets (Zhu et al, 2021), spherical fuzzy sets (Trung et al, 2022;Erdogan, 2022), rough sets (Chatterjee et al, 2018;Pamucar et al, 2017a;Bozanic et al, 2020;Pamucar et al, 2017b) and intuitionistic fuzzy sets (Ecer, 2022). For example, the MAIRCA method was combined with the AHP (Analytic Hierarchy Process) (Boral et al, 2020;Mestanza and Bakhat, 2021) and BWM (Best-Worst Methods) (Gul and Ak, 2020) approaches for obtaining criteria weights to solve the MCDM problem in classical fuzzy sets, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, in the rough set environment, Chatterjee et al (2018) designed the R'DAME-TEL (Rough Decision-Making Trial and Evaluation Laboratory) technique to structure the relationship between different criteria and utilized the R'ANP (Rough Analytic Network Process) method to calculate the weights; the R'MAIRCA (Rough MAIRCA) method was proposed to evaluate supplier performance; Bozanic et al (2020) constructed a hybrid LBWA-IR-MAIRCA (Level Based Weight Assessment-Interval Rough-MAIRCA) MCDM model to determine the weapon structure elements in an interval-valued rough set environment. Zhu et al (2021) integrated DEMATEL-MAIRCA with rough fuzzy information to evaluate biological inspiration for biologically inspired design; Ecer (2022) proposed a MAIRCA approach extended from intuitionistic fuzzy to evaluate and select COVID-19 vaccines;Trung et al (2022) advanced a hybrid MCDM model integrating AHP and MAIRCA approaches in the spherical fuzzy context; Erdogan (2022) extended the SWARA (Stepwise Weight Assessment Ratio Analysis) and MAIRCA techniques under interval-valued spherical fuzzy setting. This method has been applied to various fields by many scholars (Hashemkhani Zolfani et al, 2020;Ecer, 2021;Ecer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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A Lance Distance-Based MAIRCA Method for q-Rung Orthopair Fuzzy MCDM with Completely Unknown Weight Information

Wang¹,

Xu²,

Pamučar³

et al. 2023

Informatica

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The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

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Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

Cited by 16 publications

References 60 publications

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers

A Lance Distance-Based MAIRCA Method for q-Rung Orthopair Fuzzy MCDM with Completely Unknown Weight Information

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