Skip to main content
SpringerLink
Log in

Fine-Kinney method based on fuzzy logic for natural gas pipeline project risk assessment

  • Fuzzy systems and their mathematics
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Quality function deployment (QFD) method allows reviewing of customer requirements (CRs) and design requirements (DRs) simultaneously in order to handle the correlations and relationships of CRs and DRs in calculations. It has been often combined with fuzzy numbers, because it provides to gather the judgments of experts in vagueness environment more correctly and easily. This paper suggests employing q-rung orthopair fuzzy number (q-ROFN) for improving the fuzzy QFD approach. The proposed q-ROFN based QFD method uses q-ROFN to adjust the importance degrees of CRs based the relationships between CRs and DRs with the help of the correlations and relationships of CRs and DRs. q-ROFN presents more information than numbers such as intuitionistic fuzzy number and Pythagorean fuzzy number about the correlations and relationships of CRs and DRs. Furthermore, this work proposes integrating Fine-Kinney method and QFD method to cope with limitations of Fine-Kinney method. VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje) approach based on q-ROFN is also used to rank DRs. The proposed integrated method provides a novel decision support model for the natural gas pipeline project (NGPP) risk assessment. There is no study about the integration of Fine-Kinney, q-ROFN-based QFD, VIKOR methods. The most significant DR and the second most important DR for NGPP risk assessment are electrical sparks and coating type factor, respectively. The results of this model present a road map to the managers of NGPP for an occupational health and safety policy by ordering the failure modes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  • Akao Y (1990) Quality function deployment: integrating customer requirements into product design. Taylor & Francis

    Google Scholar 

  • Akbaş H, Bilgen B (2017) An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs. Energy 125:484–497

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Can GF, Toktas P (2021) An advanced stochastic risk assessment approach proposal based on KEMIRA-M, QFD and fine-Kinney hybridization. Int J Inf Technol Decis Mak 20(1):431–468

    Article  Google Scholar 

  • Chen LH, Chen CN (2014) Normalisation models for prioritising design requirements for quality function deployment processes. Int J Prod Res 52(2):299–313

    Article  Google Scholar 

  • Cornot-Gandolphe S, Appert O, Dickel R, Chabrelie MF, Rojey A (2003) The challenges of further cost reductions for new supply options (pipeline, LNG, GTL). In: 22nd World Gas Conference, Vol. 5, pp 1–17

  • Du WS (2018) Minkowski-type distance measures for generalized orthopair fuzzy sets. Int J Intell Syst 33(4):802–817

    Article  Google Scholar 

  • Efe B (2019a) Fuzzy cognitive map based quality function deployment approach for dishwasher machine selection. Appl Soft Comput 83:105660

    Article  Google Scholar 

  • Efe B (2019b) Analysis of operational safety risks in shipbuilding using failure mode and effect analysis approach. Ocean Eng 187:106214

    Article  Google Scholar 

  • Efe B, Efe ÖF (2021) Quality function deployment based failure mode and effect analysis approach for risk evaluation. Neural Comput Appl 33:10159–10174

    Article  Google Scholar 

  • Efe ÖF, Efe B (2022) A decision support model based on q-rung orthopair fuzzy number for glove design application. Neural Comput Appl 34:12695–12708

    Article  Google Scholar 

  • Efe B, Efe ÖF, Kurt M (2018) Ergonomik ürün tasarımına bütünleşik bir yaklaşım. SAÜ Fen Bilimleri Enstitüsü Dergisi 22(2):192–202

    Google Scholar 

  • Efe B, Yerlikaya MA, Efe ÖF (2020) Mobile phone selection based on a novel quality function deployment approach. Soft Comput 24(20):15447–15461

    Article  Google Scholar 

  • Efe B, Efe ÖF, Ishizaka A (2022) A model proposal to examine the effects of ships to marine pollution in terms of internal and external factors. Soft Comput 26(5):2121–2134

    Article  Google Scholar 

  • Gündoğdu FK, Kahraman C (2020) A novel spherical fuzzy QFD method and its application to the linear delta robot technology development. Eng Appl Artif Intell 87:103348

    Article  Google Scholar 

  • Kailasam S, Achanta SDM, Rao PRK, Vatambeti R, Kayam S (2021) An IoT-based agriculture maintenance using pervasive computing with machine learning technique. Int J Intell Comput Cybern 15:184–197

    Article  Google Scholar 

  • Kinney GF, Wiruth AD (1976) Practical risk analysis for safety management. Naval Weapons Center China Lake

    Google Scholar 

  • Lima-Junior FR, Carpinetti LCR (2016) A multicriteria approach based on fuzzy QFD for choosing criteria for supplier selection. Comput Ind Eng 101:269–285

    Article  Google Scholar 

  • Liu P, Wang P (2018) Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. Int J Intell Syst 33(2):259–280

    Article  Google Scholar 

  • Narbel PA, Hansen JP (2014) Estimating the cost of future global energy supply. Renew Sustain Energy Rev 34:91–97

    Article  Google Scholar 

  • Onar SÇ, Büyüközkan G, Öztayşi B, Kahraman C (2016) A new hesitant fuzzy QFD approach: an application to computer workstation selection. Appl Soft Comput J 46:1–16

    Article  Google Scholar 

  • Opricovic S (1998) Multi-criteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade

    Google Scholar 

  • Rojey A, Jaffret C, Cornot-Gandolp S, Durand B (1997) Natural gas production, processing, transport. Editions Technip, Paris

    Google Scholar 

  • Sampath Dakshina Murthy A, Karthikeyan T, Vinoth Kanna R (2022) Gait-based person fall prediction using deep learning approach. Soft Comput 26:12933–12941

  • Soldo B (2012) Forecasting natural gas consumption. Appl Energy 92:26–37

    Article  Google Scholar 

  • Wu SM, Liu HC, Wang LE (2017) Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle. Int J Prod Res 55(15):4436–4449

    Article  Google Scholar 

  • Yager RR (2017) Generalized orthopair fuzzy sets. IEEE Trans Fuzzy Syst 25(5):1222–1230

    Article  Google Scholar 

  • Yang Q, Chen ZS, Chan CY, Pedrycz W, Martínez L, Skibniewski MJ (2022) Large-scale group decision-making for prioritizing engineering characteristics in quality function deployment under comparative linguistic environment. Appl Soft Comput 127:109359

    Article  Google Scholar 

  • Yu L, Wang L, Bao Y (2018) Technical attributes ratings in fuzzy QFD by integrating interval-valued intuitionistic fuzzy sets and Choquet integral. Soft Comput 22(6):2015–2024

    Article  MATH  Google Scholar 

  • Zhang X, Su J, Herrera-Viedma E (2022) A decision support model for estimating participation-oriented designs of crowdsourcing platforms based on quality function deployment. Expert Syst Appl 202:117308

    Article  Google Scholar 

Download references

Funding

This study was not funded by any institution.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Necmettin Erbakan University, 42060, Konya, Turkey

    Burak Efe

  2. Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey

    Ömer Faruk Efe

Authors
  1. Burak Efe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ömer Faruk Efe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Burak Efe.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Efe, B., Efe, Ö.F. Fine-Kinney method based on fuzzy logic for natural gas pipeline project risk assessment. Soft Comput 27, 16465–16482 (2023). https://doi.org/10.1007/s00500-023-09108-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09108-6

Keywords

Search

Navigation