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Interval-Valued Pythagorean Fuzzy QFD Design Weighted by Best-Worst Method: An Application to E-Scooter Design
İrem Otay ORCID icon link to view author İrem Otay details   Cengiz Kahraman ORCID icon link to view author Cengiz Kahraman details  

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https://doi.org/10.15388/25-INFOR615
Pub. online: 5 January 2026      Type: Research Article      Open accessOpen Access

Received
1 March 2025
Accepted
1 December 2025
Published
5 January 2026

Abstract

Quality Function Deployment (QFD) is a technique used to collect Customer Requirements (CRs) for the product to be designed before the start of the manufacturing processes, and also used to determine whether CRs will be met with correlated or uncorrelated Design Requirements (DRs). In QFD technique, customers tend to explain their expectations from the product by using linguistic expressions instead of using exact numbers. Vagueness and impreciseness in linguistic expressions can be captured perfectly using fuzzy set theory. Pythagorean fuzzy (PF) sets as one of the extensions of ordinary fuzzy sets offer the decision maker a larger membership and non-membership assignment region than ordinary intuitionistic fuzzy sets. In this paper, customer requirements in QFD analysis are prioritized by Best-Worst Method (BWM), which has become a very popular optimization-based weighting method in recent years. In the proposed BWM and QFD methodology, interval-valued Pythagorean fuzzy (IVPF) sets are used for the first time in order to handle the uncertainties in the linguistic judgments. In the application, the two-phase IVPF methodology is proposed to a real life e-scooter design problem addressing 12 customer & 12 design requirements. The proposed PF methodology could determine the weights of customer requirements, and identify which of the design requirements is stronger, and make a competitive analysis to reveal the position of our company in the market under fuzzy environment. Besides, the sensitivity and comparative analyses have demonstrated the dominance of our company over the other competitors.

References

 
Akbaş, H., Bilgen, B. (2014). An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs. Energy, 125, 484–497. https://doi.org/10.1016/j.energy.2017.02.153.
 
Akram, M., Fatima, U., Alcantud, J.C.R. (2024). Group decision-making method based on Pythagorean fuzzy rough numbers. Journal of Applied Mathematics and Computing, 71, 2179–2210. https://doi.org/10.1007/s12190-024-02317-8.
 
Alimohammadlou, M., Khoshsepehr, Z. (2022). Green-resilient supplier selection: a hesitant fuzzy multi-criteria decision-making model. Environment, Development and Sustainability, 27(9), 22107–22143. https://doi.org/10.1007/s10668-022-02454-9.
 
Alimohammadlou, M., Sharifian, S. (2023). Industry 4.0 implementation challenges in small- and medium-sized enterprises: an approach integrating interval type-2 fuzzy BWM and DEMATEL. Soft Computing, 27(1), 169–186. https://doi.org/10.1007/s00500-022-07569-9.
 
Alkan, N., Kahraman, C. (2022). Prioritization of factors affecting the digitalization of quality management using interval-valued intuitionistic fuzzy best-worst method. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation, INFUS 2021, Lecture Notes in Networks and Systems, Vol. 308. Springer, Cham, pp. 28–39. https://doi.org/10.1007/978-3-030-85577-2_4.
 
Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, New York, Heidelberg.
 
Aydin, N., Seker, S., Deveci, M., Ding, W., Delen, D. (2023). A linear programming-based QFD methodology under fuzzy environment to develop sustainable policies in apparel retailing industry. Journal of Cleaner Production, 387. https://doi.org/10.1016/j.jclepro.2023.135887.
 
Chao, X., Liu, J., Ma, Z., Tu, Y., Lev, B. (2024). Security assessment and diagnosis for industrial water resources using TODIM sort considering Best–Worst Method with double hierarchy hesitant fuzzy linguistic term set. Environmental Research, 259, 119539. https://doi.org/10.1016/j.envres.2024.119539.
 
Chen, Z.-H., Wu, D.-F., Luo, W., Cheng, X.-J. (2024). A hybrid heterogeneous framework for medical waste disposal evaluation by fusing group BWM and regret-rejoice MABAC. Expert Systems with Applications, 249(Part A), 123514. https://doi.org/10.1016/j.eswa.2024.123514.
 
Dat, L.Q., Phuong, T.T., Kao, H.P., Chou, S.Y., Nghia, P.V. (2015). A new integrated fuzzy QFD approach for market segments evaluation and selection. Applied Mathematical Modelling, 39(13), 3653–3665. https://doi.org/10.1016/j.apm.2014.11.051.
 
Deniz, R., Aydin, N. (2024). Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework. Neural Computing and Applications, 36, 15685–15703. https://doi.org/10.1007/s00521-024-09788-7.
 
Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the critic method. Computers and Operations Research, 7(22), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H.
 
Edwards, W., Barron, F.H. (1994). SMARTS and SMARTER: improved simple methods for multi-attribute utility measurement. Organizational Behavior and Human Decision Processes, 60(3), 306–325. https://doi.org/10.1006/obhd.1994.1087.
 
Efe, Ö.F., Efe, B. (2022). A decision support model based on q-rung orthopair fuzzy number for glove design application. Neural Computing and Applications, 34(15), 12695–12708. https://doi.org/10.1007/s00521-022-07118-3.
 
Garg, H. (2018). New exponential operational laws and their aggregation operators for interval-valued Pythagorean fuzzy multicriteria decision-making. International Journal of Intelligent Systems, 33(3), 653–683. https://doi.org/10.1002/int.21966.
 
Gul, M., Yucesan, M., Karci, C. (2024). A stratified Bayesian decision-making model for occupational risk assessment of production facilities. Engineering Applications of Artificial Intelligence, 133, Part C, 108283. https://doi.org/10.1016/j.engappai.2024.108283.
 
Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010.
 
Haktanir, E. (2020). Prioritization of competitive suppliers using an interval-valued Pythagorean fuzzy QFD & COPRAS methodology. Journal of Multiple-valued Logic and Soft Computing, 34(1–2), 177–199.
 
Haktanir, E., Kahraman, C. (2022). New product design using Chebyshev’s inequality based interval-valued intuitionistic Z-fuzzy QFD method. Informatica, 33(1), 1–33. https://doi.org/10.15388/22-INFOR476.
 
Haktanir, E., Kahraman, C. (2019). A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development. Computers & Industrial Engineering, 132, 361–372. https://doi.org/10.1016/j.cie.2019.04.022.
 
Karasan, A., Ilbahar, E., Cebi, S., Kahraman, C. (2022). Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology. Applied Soft Computing, 118, 108445. https://doi.org/10.1016/j.asoc.2022.108445.
 
Kersuliene, V., Zavadskas, E.K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step – wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12.
 
Kutlu Gündoğdu, F., Kahraman, C. (2020). A novel spherical fuzzy QFD method and its application to the linear delta robot technology development. Engineering Applications of Artificial Intelligence, 87, 103348. https://doi.org/10.1016/j.engappai.2019.103348.
 
Li, W., Yüksel, S., Dinçer, H. (2022). Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers. Financial Innovation, 8(1), 67. https://doi.org/10.1186/s40854-022-00372-3.
 
Liu, F., Liu, Y., Abdullah, S. (2021a). Three-way decisions with decision-theoretic rough sets based on covering-based q-rung orthopair fuzzy rough set model. Journal of Intelligent & Fuzzy Systems, 5(40), 9765–9785. https://doi.org/10.3233/JIFS-202291.
 
Liu, H.-C., Wu, S.-M., Wang, Z.-L., Li, X.-Y. (2021b). A new method for quality function deployment with extended prospect theory under hesitant linguistic environment. IEEE Transactions on Engineering Management, 68(2), 442–451. https://doi.org/10.1109/TEM.2018.2864103. 8704868.
 
Liu, P., Gao, H., Ma, J. (2019). Novel green supplier selection method by combining quality function deployment with partitioned Bonferroni mean operator in interval type-2 fuzzy environment. Information Sciences, 490, 292–316. https://doi.org/10.1016/j.ins.2019.03.079.
 
Liu, P., Pan, Q., Xu, H., Zhu, B. (2022). An Extended QUALIFLEX method with comprehensive weight for green supplier selection in normal q-rung orthopair fuzzy environment. International Journal of Fuzzy Systems, 24(5), 2174–2202. https://doi.org/10.1007/s40815-021-01234-3.
 
Majumder, P., Baidya, D., Majumder, M. (2021). Application of novel intuitionistic fuzzy BWAHP process for analysing the efficiency of water treatment plant. Neural Computing and Applications, 33, 17389–17405. https://doi.org/10.1007/s00521-021-06326-7.
 
Mizuno, S., Akao, Y. (Eds.) (1978). Quality Function Deployment: A Company-wide Quality Approach. JUSE Press, Tokyo (in Japanese).
 
Mou, Q., Xu, Z., Liao, H. (2017). A graph based group decision making approach with intuitionistic fuzzy preference relations. Computers and Industrial Engineering, 110, 138–150. https://doi.org/10.1016/j.cie.2017.05.033.
 
Norouzi, A., Hajiagha, H.R. (2021). An interval type-2 hesitant fuzzy best-worst method. Journal of Intelligent and Fuzzy Systems, 40(6), 11625–11652. https://doi.org/10.3233/JIFS-202801.
 
Onar, S.C., Büyüközkan, G., Öztayşi, B., Kahraman, C. (2016). A new hesitant fuzzy QFD approach: an application to computer workstation selection. Applied Soft Computing, 46, 1–16. https://doi.org/10.1016/j.asoc.2016.04.023.
 
Otay, I., Çevik Onar, S., Öztayşi, B., Kahraman, C. (2024). Evaluation of sustainable energy systems in smart cities using a multi-expert Pythagorean fuzzy BWM & TOPSIS methodology. Expert Systems with Applications, 250, 123874. https://doi.org/10.1016/j.eswa.2024.123874.
 
Otay, I., Jaller, M. (2019). Multi-criteria & multi-expert wind power farm location selection using a Pythagorean fuzzy analytic hierarchy process. In: Proceedings of The International Conference on Intelligent and Fuzzy Systems (INFUS2019). Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making, Vol. 1029, Istanbul, Turkey, 23rd–25th July 2019, pp. 905–914.
 
Peng, X., Yang, Y. (2015). Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators. International Journal of Intelligent Systems. https://doi.org/10.1002/int.21790.
 
Pérez-Domínguez, L., Rodríguez-Picón, L.A., Alvarado-Iniesta, A., Luviano Cruz, D., Xu, Z. (2018). MOORA under Pythagorean fuzzy set for multiple criteria decision making. Complexity https://doi.org/10.1155/2018/2602376.
 
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009.
 
Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.
 
Saaty, T.L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh.
 
Seikh, M.R., Chatterjee, P. (2024). Identifying sustainable strategies for electronic waste management utilizing confidence-based group decision-making method in interval valued Fermatean fuzzy environment. Engineering Applications of Artificial Intelligence, 135, 108701. https://doi.org/10.1016/j.engappai.2024.108701.
 
Seker, S., Aydin, N. (2023). Fermatean fuzzy based Quality Function Deployment methodology for designing sustainable mobility hub center. Applied Soft Computing, 134, 110001. https://doi.org/10.1016/j.asoc.2023.110001.
 
Sonawane, K.S., Pawar, H.U., Aher, H.R. (2025). Design and optimization of a lightweight electric bike chassis using FEA and composite materials. International Journal of Research Publications, 6(1), 1071–1076.
 
Song, W., Ming, X., Han, Y. (2014). Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach. International Journal of Production Research, 52(18), 5528–5545. https://doi.org/10.1080/00207543.2014.917213.
 
Sumrit, D., Keeratibhubordee, J. (2024). An integrated SWARA-QFD under Fermatean fuzzy set approach to assess proactive risk mitigation strategies in recycling supply chain: case study of plastic recycling industry. Journal of Engineering Research, 13(2), 492–510. https://doi.org/10.1016/j.jer.2023.11.007.
 
Tavana, M., Shaabani, A., Di Caprio, D., Bonyani, A. (2022). A novel Interval Type-2 Fuzzy best-worst method and combined compromise solution for evaluating eco-friendly packaging alternatives. Expert Systems with Applications, 200, 117188. https://doi.org/10.1016/j.eswa.2022.117188.
 
Tavana, M., Shaabani, A., Mohammadabadi, S.M., Varzgani, N. (2021). An integrated fuzzy AHP-fuzzy MULTIMOORA model for supply chain risk-benefit assessment and supplier selection. International Journal of Systems Science: Operations & Logistics, 3(8), 238–261. https://doi.org/10.1080/23302674.2020.1737754.
 
Torrisi, V., Camporeale, R., Moslem, S., Campisi, T. (2025). FUCOM-Kendall model for evaluating E-scooter adoption: a case study in Catania, Italy. Transportation Research Procedia, 86, 1–8. https://doi.org/10.1016/j.trpro.2025.04.001.
 
Van, L.H., Yu, V.F., Dat, L.Q., Dung, C.C., Chou, S.-Y., Loc, N.V. (2018). New integrated quality function deployment approach based on interval neutrosophic set for green supplier evaluation and selection. Sustainability, 10(3), 838. https://doi.org/10.3390/su10030838.
 
Wang, H., Fang, Z., Wang, D., Liu, S. (2020). An integrated fuzzy QFD and grey decision-making approach for supply chain collaborative quality design of large complex products. Computers & Industrial Engineering, 140, 106212. https://doi.org/10.1016/j.cie.2019.106212.
 
Wang, J., Liu, H.-C., Shi, H., Guo, W., Zhu, J.-Y. (2023). New approach for quality function deployment based on social network analysis and interval 2-tuple Pythagorean fuzzy linguistic information. Computers and Industrial Engineering, 183, 109554. https://doi.org/10.1016/j.cie.2023.109554.
 
Wu, T., Liu, X., Qin, J., Herrera, F. (2021). An interval type-2 fuzzy Kano-prospect-TOPSIS based QFD model: application to Chinese e-commerce service design. Applied Soft Computing, 111, 107665. https://doi.org/10.1016/j.asoc.2021.107665.
 
Wu, T-H., Chen, C-H., Mao, N., Lu, S-T. (2017). Fishmeal supplier evaluation and selection for aquaculture enterprise sustainability with a fuzzy MCDM approach. Symmetry, 9, 286. https://doi.org/10.3390/sym9110286.
 
Yager, R.R. (2013). Pythagorean fuzzy subsets. In: Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375.
 
Yager, R.R. (2016). Properties and applications of Pythagorean fuzzy sets. In: Imprecision and Uncertainty in Information Representation and Processing. Springer, Cham, pp. 119–136. https://doi.org/10.1007/978-3-319-26302-1_9.
 
Yang, Y., Tian, Z.P., Lin, J. (2024). Strategic outsourcing in reverse logistics: neutrosophic integrated approach with a hierarchical and interactive quality function deployment. Applied Soft Computing, 152, 111256. https://doi.org/10.1016/j.asoc.2024.111256.
 
Yazdani, M., Kahraman, C., Zarate, P., Onar, S.C. (2019). A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis. Expert Systems with Applications, 115, 474–485. https://doi.org/10.1016/j.eswa.2018.08.017.
 
Yu, L., Wang, L., Bao, Y. (2018). Technical attributes ratings in fuzzy QFD by integrating interval-valued intuitionistic fuzzy sets and choquet integral. Soft Computing, 22(6), 2015–2024. https://doi.org/10.1007/s00500-016-2464-8.
 
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.

Biographies

Otay İrem
https://orcid.org/0000-0001-5895-506X
irem.otay@bilgi.edu.tr

İ. Otay has been working for Department of Industrial Engineering in Istanbul Bilgi University since 2019 and head of the department since 2024. She pursued a BS degree in industrial engineering at Yildiz Technical University in 2006, an MBA degree at Bahcesehir University in 2009, and a PhD degree in management engineering at Istanbul Technical University in 2015. She worked at University of California Davis (UC Davis) on a research project for almost a year as a Visiting Scholar. The name of the project is “Modelling Humanitarian Logistics Resource Allocation Problem during the Post-disaster Considering Uncertainty”. Her main research areas are the fuzzy sets and their applications, multi-criteria decision making, and mathematical programming. She gives lectures on operations research, production planning and productivity management at the undergraduate and graduate levels. Her works have been published in international journals and conference proceedings. She also edited an international book from Springer. According to Google Scholar, she has more than 1340 citations, has an h-index of 18, and an i-10 index of 27.

Kahraman Cengiz
https://orcid.org/0000-0001-6168-8185
kahramanc@itu.edu.tr

C. Kahraman was born in Üsküdar in 1965. He started his higher education at Istanbul Technical University, Department of Industrial Engineering in 1983. He started working as a research assistant in this department, where he graduated in 1988. He received his undergraduate degree in Industrial Engineering from ITU in 1988, his master’s degree in industrial engineering in 1990, and his doctorate degree in industrial engineering in 1996. Prof. Kahraman received the title of assistant professor in 1996, associate professor in 1998, and professor in 2003 from Istanbul Technical University. Prof. Dr. Cengiz Kahraman’s main research areas include engineering economics, quality management, multi-criteria decision making, statistical decision making and fuzzy decision making. Prof. Kahraman has authored more than 300 internationally indexed articles, more than 230 international conference papers, and more than 100 international book chapters. He edited 25 international books and guest-edited special issues of many international magazines. He currently sits on the editorial boards of 20 international journals, including one as editor-in-chief. Cengiz Kahraman, who chair many international scientific conferences such as INFUS coferences, also served as the Vice Dean of ITU Faculty of Business Administration between 2004–2007 and as the Head of ITU Industrial Engineering Department between 2007–2010, and Rector Advisor between 2022–2024.


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Keywords
interval-valued Pythagorean fuzzy sets QFD House of Quality Best-Worst Method Multi-Criteria Decision-Making (MCDM) Multi-Attribute Decision-Making (MADM) E-scooter design

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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