Skip to main content
SpringerLink
Log in

Delivery Drone Design Using Spherical Fuzzy Quality Function Deployment

  • Chapter
  • First Online:
Decision Making with Spherical Fuzzy Sets

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 392))

Abstract

Quality Function Deployment (QFD) is a well-known structured approach meeting customer needs through technical applications. Customer requirements (CRs) for a delivery drone design are first determined and they are tried to be satisfied by design requirements (DRs) in this chapter. Uncertainty in this process is handled by fuzzy set theory. One of the extensions of ordinary fuzzy sets is spherical fuzzy sets (SFSs) theory proposed by Kutlu Gündoğdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019), which is composed of those independent parameters: membership degree, non-membership degree, and hesitancy degree, satisfying that their squared sum is equal to at most 1. We employ Spherical fuzzy QFD (SF-QFD) in the design of delivery drones. The importance ratings and global weights of CRs and improvement directions of DRs are represented by SFSs. Spherical fuzzy aggregation operators are used to aggregate the opinions of different decision makers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Akao Y (1972) New product development and quality assurance deployment system (in Japanese). Standard Q Control 25(4):243–246

    Google Scholar 

  • Alptekin SE, Alptekin GI (2018) A fuzzy quality function deployment approach for differentiating cloud products. Int J Comput Intell Syst 11(1):1041–1055

    Article  Google Scholar 

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MATH  Google Scholar 

  • Bevilacqua M, Ciarapica FE, Marchetti B (2012) Development and test of a new fuzzy-QFD approach for characterizing customers rating of extra virgin olive oil. Food Qual Prefer 24(1):75–84

    Article  Google Scholar 

  • Cebi S, Ozkok M, Demirci E (2014) Evaluation of design parameters for vessel engine room by using a modified QFD technique. J Multiple-Valued Logic Soft Comput 23(5–6):559–587

    Google Scholar 

  • Chen L, Ko W (2009) Fuzzy linear programming models for new product design using QFD with FMEA. Appl Math Model 33(2):633–647

    Article  MATH  Google Scholar 

  • Chen L, Ko W (2011) Fuzzy nonlinear models for new product development using four-phase quality function deployment processes. IEEE Trans Syst Man Cybern Part A Syst Hum 41(5):927–945

    Article  Google Scholar 

  • Chen L, Weng M (2006) An evaluation approach to engineering design in QFD processes using fuzzy goal programming models. Eur J Oper Res 172(1):230–248

    Article  MATH  Google Scholar 

  • Chen Y, Tang J, Fung RYK, Ren Z (2004) Fuzzy regression-based mathematical programming model for quality function deployment. Int J Prod Res 42(5):1009–1027

    Article  MATH  Google Scholar 

  • Chen Y, Fung RYK, Tang J (2005) Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD. Int J Prod Res 43(17):3583–3604

    Article  MATH  Google Scholar 

  • Delice EK, Güngör Z (2013) Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system. Int J Prod Res 51(21):6378–6396

    Article  Google Scholar 

  • Ejegwa PA (2019) Modified Zhang and Xu’s distance measure for Pythagorean fuzzy sets and its application to pattern recognition problems. Neural Comput Appl 1–10

    Google Scholar 

  • Fung RYK, Chen Y, Chen LI, Tang J (2005) A fuzzy expected value-based goal programing model for product planning using quality function deployment. Eng Optim 37(6):633–647

    Article  Google Scholar 

  • Gündoğdu FK (2019) Principals of spherical fuzzy sets. In: International conference on intelligent and fuzzy systems. Springer, Cham, pp 15–23

    Google Scholar 

  • Gündoğdu FK, Kahraman C (2019a) A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets. Eng Appl Artif Intell 85:307–323

    Article  Google Scholar 

  • Gündoğdu FK, Kahraman C (2019) A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Comput 1–15

    Google Scholar 

  • Haktanır E, Kahraman C (2019) A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development. Comput Ind Eng 132:361–372

    Article  Google Scholar 

  • Hsu C, Chang A, Kuo H (2011) Green supply implementation based on fuzzy QFD: An application in GPLM system. WSEAS Trans Syst 10(6):183–192

    Google Scholar 

  • Huang J, You X, Liu H, Si S (2019) New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory. Int J Prod Res 57(5):1283–1299

    Article  Google Scholar 

  • Hundal GPS, Kant S (2017) Product design development by integrating QFD approach with heuristics-AHP, ANN and fuzzy logics-a case study in miniature circuit breaker. Int J Prod Q Manage 20(1):1–28

    Google Scholar 

  • Kahraman C, Ertay T, Büyüközkan G (2006) A fuzzy optimization model for QFD planning process using analytic network approach. Eur J Oper Res 171(2):390–411

    Article  MATH  Google Scholar 

  • Kahraman C, Gundogdu FK, Onar SC, Oztaysi B (2019) Hospital location selection using spherical fuzzy TOPSIS. In: 2019 Conference of the international fuzzy systems association and the European society for fuzzy logic and technology (EUSFLAT 2019). Atlantis Press

    Google Scholar 

  • Kalargeros N, Gao JX (1998) QFD: focusing on its simplification and easy computerization using fuzzy logic principles. Int J Veh Des 19(3):315–325

    Google Scholar 

  • Kang X, Yang M, Wu Y, Ni B (2018) Integrating evaluation grid method and fuzzy quality function deployment to new product development. Math Probl Eng 2018

    Google Scholar 

  • Karimi BH, Mozafari MM, Asli MN (2012) Applying a hybrid QFD-TOPSIS method to design product in the industry (case study in sum service company). Res J Appl Sci Eng Technol 4(18):3283–3288

    Google Scholar 

  • Karsak EE (2004) Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment. Int J Prod Res 42(18):3957–3974

    Article  MATH  Google Scholar 

  • Kavosi M, Mavi RK (2011) Fuzzy quality function deployment approach using TOPSIS and analytic hierarchy process methods. Inter J Prod Q Manage 7(3):304–324

    Google Scholar 

  • Kuo T, Wu H, Shieh J (2009) Integration of environmental considerations in quality function deployment by using fuzzy logic. Expert Syst Appl 36(3 PART 2):7148–7156

    Article  Google Scholar 

  • Kutlu Gundogdu F, Kahraman C (2019) Extension of WASPAS with spherical fuzzy sets. Informatica 30(2):269–292

    Article  Google Scholar 

  • Kutlu Gündoğdu F, Kahraman C (2019a) Spherical fuzzy sets and spherical fuzzy TOPSIS method. J Intell Fuzzy Syst 36(1):337–352

    Article  Google Scholar 

  • Kutlu Gündoğdu F, Kahraman C (2019b) A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection. J Intell Fuzzy Syst 37(1):1197–1211

    Article  Google Scholar 

  • Kutlu Gündoğdu F, Kahraman C (2020) A novel spherical fuzzy QFD method and its application to the linear delta robot technology development. Eng Appl Artif Intell 87

    Google Scholar 

  • Kwong CK, Bai H (2002) A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J Intell Manuf 13(5):367–377

    Article  Google Scholar 

  • Lee Y, Sheu L, Tsou Y (2008) Quality function deployment implementation based on fuzzy kano model: an application in PLM system. Comput Ind Eng 55(1):48–63

    Article  Google Scholar 

  • Lee AHI, Kang H, Yang C, Lin C (2010) An evaluation framework for product planning using FANP, QFD and multi-choice goal programming. Int J Prod Res 48(13):3977–3997

    Article  MATH  Google Scholar 

  • Lee Z, Pai C, Yang C (2012) Customer needs and technology analysis in new product development via fuzzy QFD and delphi. WSEAS Trans Bus Econ 9(1):1–15

    Google Scholar 

  • Li M (2013) The method for product design selection with incomplete linguistic weight information based on quality function deployment in a fuzzy environment. Math Probl Eng 2013(7)

    Google Scholar 

  • Li S, Tang D, Wang Q (2019) Rating engineering characteristics in open design using a probabilistic language method based on fuzzy QFD. Comput Ind Eng 135:348–358

    Article  Google Scholar 

  • Liu S (2005) Rating design requirements in fuzzy quality function deployment via a mathematical programming approach. Int J Prod Res 43(3):497–513

    Article  MATH  Google Scholar 

  • Liu H (2009) The extension of fuzzy QFD: From product planning to part deployment. Expert Syst Appl 36(8):11131–11144

    Article  Google Scholar 

  • Liu H (2011) Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Appl Math Model 35(1):482–496

    Article  MATH  Google Scholar 

  • Liu A, Hu H, Zhang X, Lei D (2017) Novel two-phase approach for process optimization of customer collaborative design based on fuzzy-QFD and DSM. IEEE Trans Eng Manage 64(2):193–207

    Article  Google Scholar 

  • Luo XG, Kwong CK, Tang JF (2010) Determining optimal levels of engineering characteristics in quality function deployment under multi-segment market. Comput Ind Eng 59(1):126–135

    Article  Google Scholar 

  • Mizuno S, Akao Y (1978) Quality function deployment: a company-wide quality approach (in Japanese). JUSE Press, Tokyo

    Google Scholar 

  • Nepal B, Yadav OP, Murat A (2010) A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development. Expert Syst Appl 37(10):6775–6786

    Article  Google Scholar 

  • Peng J, Xia G, Sun B, Wang S (2018) Systematical decision-making approach for quality function deployment based on uncertain linguistic term sets. Int J Prod Res 56(18):6183–6200

    Article  Google Scholar 

  • Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539

    MATH  Google Scholar 

  • Verma D, Chilakapati R, Fabrycky WJ (1998) Analyzing a quality function deployment matrix: An expert system-based approach to identify inconsistencies and opportunities. J Eng Des 9(3):250–261

    Article  Google Scholar 

  • Vinodh S, Rathod G (2012) Application of fuzzy logic-based environmental conscious QFD to rotary switch: a case study. Clean Technol Environ Policy 14(2):319–332

    Article  Google Scholar 

  • Vinodh S, Manjunatheshwara KJ, Karthik Sundaram S, Kirthivasan V (2017) Application of fuzzy quality function deployment for sustainable design of consumer electronics products: a case study. Clean Technol Environ Policy 19(4):1021–1030

    Article  Google Scholar 

  • Wang J (1999) Fuzzy outranking approach to prioritize design requirements in quality function deployment. Int J Prod Res 37(4):899–916

    Article  MATH  Google Scholar 

  • Wang Y (2012) A fuzzy-normalisation-based group decision-making approach for prioritising engineering design requirements in QFD under uncertainty. Int J Prod Res 50(23):6963–6977

    Article  Google Scholar 

  • Wang C (2019) Integrating a novel intuitive fuzzy method with quality function deployment for product design: case study on touch panels. J Intell Fuzzy Syst 37(2):2819–2833

    Article  Google Scholar 

  • Wang C, Chen J (2012) Using quality function deployment for collaborative product design and optimal selection of module mix. Comput Ind Eng 63(4):1030–1037

    Article  Google Scholar 

  • Wang D, Yu H, Wu J, Meng Q, Lin Q (2019) Integrating fuzzy based QFD and AHP for the design and implementation of a hand training device. J Intell Fuzzy Syst 36(4):3317–3331

    Article  Google Scholar 

  • Wu Q (2011) Fuzzy measurable house of quality and quality function deployment for fuzzy regression estimation problem. Expert Syst Appl 38(12):14398–14406

    Article  Google Scholar 

  • Wu Y, Ho CC (2015) Integration of green quality function deployment and fuzzy theory: a case study on green mobile phone design. J Clean Prod 108:271–280

    Article  Google Scholar 

  • Kutlu Gündoğdu F (Preprint) A spherical fuzzy extension of MULTIMOORA method. J Intell Fuzzy Syst 1–16

    Google Scholar 

  • Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965

    Article  Google Scholar 

  • Yang CL, Fang HH (2003) Integrating fuzzy logic into quality function deployment for product positioning. J Chin Inst Ind Eng 20(3):275–281

    Google Scholar 

  • Zadeh LA (1965) Fuzzy Sets. Information and Control 338–353

    Article  MathSciNet  MATH  Google Scholar 

  • Zhai L, Khoo L, Zhong Z (2009) A rough set based QFD approach to the management of imprecise design information in product development. Adv Eng Inform 23(2):222–228

    Article  Google Scholar 

  • Zheng P, Xu X, Xie SQ (2019) A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning. J Intell Manuf 30(1):3–16

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Altınbas University, 34217, Bagcilar, Istanbul, Turkey

    Elif Haktanır

  2. Industrial Engineering Department, Istanbul Technical University, Besiktas, Istanbul, 34367, Turkey

    Cengiz Kahraman

  3. Turkish Air Force Academy, National Defence University, Yesilyurt, Istanbul, 34367, Turkey

    Fatma Kutlu Gündoğdu

Authors
  1. Elif Haktanır
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cengiz Kahraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fatma Kutlu Gündoğdu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elif Haktanır .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Turkish Air Force Academy, National Defence University, Istanbul, Turkey

    Fatma Kutlu Gündoğdu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Haktanır, E., Kahraman, C., Kutlu Gündoğdu, F. (2021). Delivery Drone Design Using Spherical Fuzzy Quality Function Deployment. In: Kahraman, C., Kutlu Gündoğdu, F. (eds) Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-45461-6_17

Download citation

Publish with us

Policies and ethics

Search

Navigation