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

Abstract

Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.biological inspiration evaluation, biologically inspired design, DEMATEL, fuzzy rough number, MAIRCA | INTRODUCTIONBiologically inspired (i.e., bio-inspired) design (BID) is a promising and rapidly increasing paradigm for design innovation and sustainable design. 1 For millions of years, a huge number of biological functions, behaviors, and structures have been evolved in nature, providing a robust and efficient manner that enables plants and animals to sustain their existence in diverse ways. 2 As a knowledge-driven design methodology, BID aims to systematically mine biological knowledge (e.g., biological functions, behaviors, and structures) to resolve engineering problems, such as eco-design and design creativity-inspiring. 3 It starts with the problem formulation and follows with the problem reframing, biological inspiration identification (i.e., biological solution acquisition, biological solution definition, biological inspiration generation, and biological inspiration evaluation), and the final biological inspiration application. 4 Due to its prominent performance in creativity-inspiring and technical innovation, BID has been widely investigated in various areas and a vast amount of biomimetic applications have emerged in our daily life, such as algorithm development, 5 robotics, 6 material design, 7 optics, 8 and image processing. 9 BID is inherently interdisciplinary. 10 To facilitate the knowledge analogy between the engineering world and biological systems, designers should be equipped with a strong background in the engineering discipline as well as good expertise in the biol...

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

“…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.…”

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

“…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.…”

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

“…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).…”

“…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.…”

“…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).…”