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A partial-consensus and unequal-authority fuzzy collaborative intelligence approach for assessing robotic applications amid the COVID-19 pandemic

  • Fuzzy systems and their mathematics
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Abstract

Assessing and comparing the overall performances of robotic applications amid the COVID-19 pandemic is a key task for local governments and relevant stakeholders, but has yet to be investigated. To accomplish this task, this study proposes a partial-consensus and unequal-authority fuzzy collaborative intelligence approach. In the proposed methodology, each evaluator first uses fuzzy geometric mean (FGM) to derive the fuzzy priorities of criteria for assessing the performance of each robotic application. Subsequently, considering the unequal authority levels of evaluators and the lack of an overall consensus, the partial-consensus fuzzy weighted intersection (PCFWI) operator is proposed to aggregate the derivation results. Finally, alpha-cut operations (ACO)-based fuzzy weighted average (FWA) is applied to evaluate the overall performance of each robotic application. The partial-consensus and unequal-authority fuzzy collaborative intelligence approach have been applied to assess the overall performances of four robotic applications amid the COVID-19 pandemic. Based on the experimental results, the Xenex LightStrike robot was named the #1 robotics application during the COVID-19 pandemic, followed by the Brain Navi Nasal Swab Robot. Furthermore, the proposed methodology outperforms three existing methods by up to 21% in preserving evaluators’ original judgments.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  • abc7news (2020) School uses virus-killing robot to keep classrooms clean amid COVID-19 pandemic. https://abc7news.com/how-to-kill-coronavirus-disinfect-against-covid-19-keeping-clean-schools/6349267/

  • Bader F, Rahimifard S (2020) A methodology for the selection of industrial robots in food handling. Innov Food Sci Emerg Technol 64:102379

    Google Scholar 

  • Bakdi A, Vanem E (2022) Fullest COLREGs evaluation using fuzzy logic for collaborative decision-making analysis of autonomous ships in complex situations. IEEE Trans Intell Transp Syst 23(10):18433–18445

    Google Scholar 

  • Bhattacharya A, Mohapatra P, Kumar V, Dey PK, Brady M, Tiwari MK, Nudurupati SS (2014) Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Prod Plan Control 25(8):698–714

    Google Scholar 

  • Bloss R (2016) Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications. Ind Robot 43(5):463–468

    Google Scholar 

  • Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247

    MathSciNet  MATH  Google Scholar 

  • Business Performance Management Singapore (2013) AHP—high consistency ratio. https://bpmsg.com/ahp-high-consistency-ratio/

  • Chan S (2020) Taiwan’s Brain Navi builds a robot for nasal swab tests amid COVID-19. https://meet.bnext.com.tw/intl/articles/view/46816

  • Chen T (2012) A hybrid fuzzy and neural approach with virtual experts and partial consensus for DRAM price forecasting. Int J Innov Comput Inf Control 8(1):583–597

    MathSciNet  Google Scholar 

  • Chen TCT (2020) Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method. Neural Comput Appl 32:7057–7068

    Google Scholar 

  • Chen T, Chiu MC (2022a) A fuzzy collaborative intelligence approach to group decision-making: a case study of post-COVID-19 restaurant transformation. Cogn Comput 14(2):531–546

    MathSciNet  Google Scholar 

  • Chen TCT, Chiu MC (2022b) Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence. Digital Health 8:20552076221136380

    Google Scholar 

  • Chen T, Lin YC (2008) A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. Int J Uncertain Fuzziness Knowl Based Syst 16(1):35–58

    Google Scholar 

  • Chen TCT, Lin YC (2020) A FAHP-FTOPSIS approach for bioprinter selection. Health Technol 10(6):1455–1467

    Google Scholar 

  • Chen TCT, Honda K (2020) Three-mode fuzzy co-clustering and collaborative framework. In: Fuzzy collaborative forecasting and clustering: methodology, system architecture, and applications, pp 73–88.

  • Chen T, Wang YC (2021) Recommending suitable smart technology applications to support mobile healthcare after the COVID-19 pandemic using a fuzzy approach. Healthcare 9(11):1461

    Google Scholar 

  • Chen T, Liao TW, Yu F (2015) Fuzzy collaborative intelligence and systems. Int J Intell Syst 30(6):617–619

    Google Scholar 

  • Chen TCT, Wang YC, Lin YC, Wu HC, Lin HF (2019) A fuzzy collaborative approach for evaluating the suitability of a smart health practice. Mathematics 7(12):1180

    Google Scholar 

  • Chen TCT, Wang YC, Lin CW (2020) A fuzzy collaborative forecasting approach considering experts’ unequal levels of authority. Appl Soft Comput 94:106455

    Google Scholar 

  • Chiu MC, Chen T (2021) Assessing mobile and smart technology applications for active and healthy aging using a fuzzy collaborative intelligence approach. Cogn Comput 13:431–446

    Google Scholar 

  • Chiu MC, Chen TCT (2022) A ubiquitous healthcare system of 3D printing facilities for making dentures: application of type-II fuzzy logic. Digital Health 8:20552076221092540

    Google Scholar 

  • Chiu MC, Chen TCT, Hsu KW (2020) Modeling an uncertain productivity learning process using an interval fuzzy methodology. Mathematics 8(6):998

    Google Scholar 

  • Choudhary D, Shankar R (2012) An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: a case study from India. Energy 42(1):510–521

    Google Scholar 

  • Crawford M (2016) Top 6 robotic applications in medicine. https://www.asme.org/topics-resources/content/top-6-robotic-applications-in-medicine

  • Dehraj P, Sharma A (2020) An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique. Appl Soft Comput 90:106137

    Google Scholar 

  • Dinçer H, Yüksel S, Martínez L (2022) Collaboration enhanced hybrid fuzzy decision-making approach to analyze the renewable energy investment projects. Energy Rep 8:377–389

    Google Scholar 

  • Etherington D (2020) MIT and Boston Dynamics team up on ‘Dr. Spot,’ a robot for remote COVID-19 vital sign measurement. https://techcrunch.com/2020/08/19/mit-and-boston-dynamics-team-up-on-dr-spot-a-robot-for-remote-covid-19-vital-sign-measurement/

  • Gao H, Ju Y, Gonzalez EDS, Zhang W (2019) Green supplier selection in electronics manufacturing: an approach based on consensus decision making. J Clean Prod 245:118781

    Google Scholar 

  • Güran A, Uysal M, Ekinci Y, Güran CB (2017) An additive FAHP based sentence score function for text summarization. Inf Technol Control 46(1):53–69

    Google Scholar 

  • Joseph MM, Ahasic AM, Clark J, Templeton K (2021) State of women in medicine: history, challenges, and the benefits of a diverse workforce. Pediatrics 148(Supplement 2):e2021051440C

    Google Scholar 

  • Kalaiselvi S, Gomathi V (2020) α-cut induced fuzzy deep neural network for change detection of SAR images. Appl Soft Comput 95:106510

    Google Scholar 

  • Kalu A (2020) COVID-19 and right to freedom of movement. https://www.vanguardngr.com/2020/04/covid-19-and-right-to-freedom-of-movement/. Read more at: https://www.vanguardngr.com/2020/04/covid-19-and-right-to-freedom-of-movement/

  • Kumar R, Garg RK (2010) Optimal selection of robots by using distance based approach method. Robot Comput Integr Manuf 26(5):500–506

    Google Scholar 

  • Lima Junior FR, Osiro L, Carpinetti LCR (2014) A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Appl Soft Comput 21:194–209

    Google Scholar 

  • Lin YC, Chen T (2020) A multibelief analytic hierarchy process and nonlinear programming approach for diversifying product designs: smart backpack design as an example. Proc Inst Mech Eng Part B J Eng Manuf 234(6–7):1044–1056

    Google Scholar 

  • Lin YC, Chen T (2021) A ubiquitous clinic recommendation system using the modified mixed-binary nonlinear programming-feedforward neural network approach. J Theor Appl Electron Commerce Res 16(7):3282–3298

    Google Scholar 

  • Lin YC, Chen TCT (2022) Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic. Digital Health 8:20552076221106320

    Google Scholar 

  • Lin YC, Wang YC, Chen TCT, Lin HF (2019) Evaluating the suitability of a smart technology application for fall detection using a fuzzy collaborative intelligence approach. Mathematics 7(11):1097

    Google Scholar 

  • Lo Scalzo F (2020) Tommy the robot nurse helps keep Italy doctors safe from coronavirus. https://www.reuters.com/article/us-health-coronavirus-italy-robots-idUSKBN21J67Y

  • Manganello K (2020) Xenex LightStrike robot destroys SARS-CoV-2 (Coronavirus) in 2 minutes. https://www.xenex.com/resources/news/xenex-lightstrike-robot-destroys-sars-cov-2-coronavirus-in-2-minutes/

  • Marr B (2020) Robots and drones are now used to fight COVID-19. https://www.forbes.com/sites/bernardmarr/2020/03/18/how-robots-and-drones-are-helping-to-fight-coronavirus/#2c4e490f2a12

  • Meah N (2020) Robot to deliver meals, medication to Covid-19 patients at Alexandra Hospital to reduce exposure of healthcare workers. https://www.todayonline.com/singapore/robot-deliver-meals-medication-covid-19-patients-alexandra-hospital-reduce-exposure

  • Meisenzahl M (2020) Softbank’s famous robot Pepper is helping enforce social distancing and greeting COVID-19 patients around the world. https://www.businessinsider.com/softbank-pepper-robot-coronavirus-japan-and-germany-2020-5

  • Nguyen PH (2022) Spherical fuzzy decision-making approach integrating Delphi and TOPSIS for package tour provider selection. Math Probl Eng 2022:4249079

    Google Scholar 

  • Pan NF (2008) Fuzzy AHP approach for selecting the suitable bridge construction method. Autom Constr 17(8):958–965

    Google Scholar 

  • Parameshwaran R, Kumar SP, Saravanakumar K (2015) An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria. Appl Soft Comput 26:31–41

    Google Scholar 

  • Park JH, Ryoo HY (2018) User perception of the home robot price. Int J Adv Sci Technol 115:87

    Google Scholar 

  • Pedrycz W (2008) Collaborative architectures of fuzzy modeling. In: IEEE world congress on computational intelligence, pp 117–139

  • Purtill C (2020) Meet Violet, the robot that can kill the COVID-19 virus. https://time.com/5825860/coronavirus-robot/

  • Rao RV, Patel BK, Parnichkun M (2011) Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robot Auton Syst 59(6):367–375

    Google Scholar 

  • Relich M, Pawlewski P (2017) A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing 231:19–27

    Google Scholar 

  • Saaty TL (1986) Axiomatic foundation of the analytic hierarchy process. Manage Sci 32(7):841–855

    MathSciNet  MATH  Google Scholar 

  • Senapati T, Chen G, Mesiar R, Yager RR (2022) Novel Aczel-Alsina operations-based interval-valued intuitionistic fuzzy aggregation operators and their applications in multiple attribute decision-making process. Int J Intell Syst 37(8):5059–5081

    Google Scholar 

  • Sirisawat P, Kiatcharoenpol T (2018) Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Comput Ind Eng 117:303–318

    Google Scholar 

  • Sparks H (2020) Can this germ-zapping robot really kill the coronavirus? https://nypost.com/2020/05/01/can-this-germ-zapping-robot-really-kill-the-coronavirus/

  • Tseng ML, Li SX, Lin CWR, Chiu AS (2023) Validating green building social sustainability indicators in China using the fuzzy delphi method. J Ind Prod Eng 40(1):35–53

    Google Scholar 

  • Van Broekhoven E, De Baets B (2006) Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets Syst 157(7):904–918

    MathSciNet  MATH  Google Scholar 

  • van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(1–3):229–241

    MathSciNet  MATH  Google Scholar 

  • Vincent J (2020) Please remain calm while the robot swabs your nose. https://www.theverge.com/2020/8/24/21377011/robot-nasal-swab-machine-autonomous-covid-19-test-brain-navi

  • Wang YC, Chen TCT (2019) A partial-consensus posterior-aggregation FAHP method—supplier selection problem as an example. Mathematics 7(2):179

    Google Scholar 

  • Wang YC, Chen T, Yeh YL (2019) Advanced 3D printing technologies for the aircraft industry: a fuzzy systematic approach for assessing the critical factors. Int J Adv Manuf Technol 105:4059–4069

    Google Scholar 

  • Wang YC, Tsai HR, Chen T (2021) A selectively fuzzified back propagation network approach for precisely estimating the cycle time range in wafer fabrication. Mathematics 9(12):1430

    Google Scholar 

  • Well S (2020) Robot taxis could solve a crucial Covid-19 problem. https://www.inverse.com/innovation/robo-taxis-take-over-post-covid

  • Winkle K, Caleb-Solly P, Turton A, Bremner P (2020) Mutual shaping in the design of socially assistive robots: a case study on social robots for therapy. Int J Soc Robot 12:847–866

    Google Scholar 

  • Wu HC, Chen T, Huang CH (2020a) A piecewise linear FGM approach for efficient and accurate FAHP analysis: smart backpack design as an example. Mathematics 8(8):1319

    Google Scholar 

  • Wu HC, Wang YC, Chen TCT (2020b) Assessing and comparing COVID-19 intervention strategies using a varying partial consensus fuzzy collaborative intelligence approach. Mathematics 8(10):1725

    Google Scholar 

  • Wu HC, Chen TCT, Chiu MC (2021) Constructing a precise fuzzy feedforward neural network using an independent fuzzification approach. Axioms 10(4):282

    Google Scholar 

  • Wu HC, Lin YC, Chen TCT (2022) Leisure agricultural park selection for traveler groups amid the COVID-19 pandemic. Agriculture 12(1):111

    Google Scholar 

  • Zanoni P (2011) Diversity in the lean automobile factory: doing class through gender, disability and age. Organization 18(1):105–127

    Google Scholar 

  • Zheng G, Zhu N, Tian Z, Chen Y, Sun B (2012) Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf Sci 50(2):228–239

    Google Scholar 

  • Zhu GN, Hu J, Ren H (2020) A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Appl Soft Comput 91:106228

    Google Scholar 

  • Zyoud SH, Kaufmann LG, Shaheen H, Samhan S, Fuchs-Hanusch D (2016) A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Syst Appl 61:86–105

    Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, 1001, University Road, Hsinchu, Taiwan

    Tin-Chih Toly Chen

  2. Department of Industrial Engineering and Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung, Taiwan

    Hsin-Chieh Wu

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  1. Tin-Chih Toly Chen
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  2. Hsin-Chieh Wu
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Both authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by T-CTC and H-CW. T-CTC wrote the first draft of the manuscript and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

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Correspondence to Hsin-Chieh Wu.

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Chen, TC.T., Wu, HC. A partial-consensus and unequal-authority fuzzy collaborative intelligence approach for assessing robotic applications amid the COVID-19 pandemic. Soft Comput 27, 16493–16509 (2023). https://doi.org/10.1007/s00500-023-09136-2

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