Skip to main content
Log in

Evaluation of Maintenance Policies Using a Two-Stage Pythagorean-Based Group Decision-Making Approach

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

The key to achieving the safety and reliability of the production system is to implement an efficient maintenance management system to reduce or eliminate equipment failures. Maintenance planning costs constitute a large part of operating costs. Determining the optimal maintenance policy plays a significant role in improving system reliability and availability as well as providing cost-savings. For this reason, it is necessary to determine and implement the most appropriate maintenance planning for the production system to ensure the reliable, smooth, and healthy operation of the system. The decision-making process is multidimensional and complicated due to the nature of the maintenace policy selection problem, so it involves various conflicting evaluation criteria in identifying the optimal maintenance policies. Multi-criteria decision-making (MCDM) instruments are appropriate for such complex issues. In this study, a hybrid-two-stage-MCDM-approach consisting of Pythagorean fuzzy Analytical Hierarchical Process (PF-AHP) and Pythagorean fuzzy TOPSIS (PF-TOPSIS) are carried out to determine the best maintenance policy, which is one of the most serious issues, both tactically and operationally, handled by one of the largest food companies in Turkey. Five main evaluation criteria (reliability, safety, cost, added-value, and feasibility), and six alternative possible maintenance policies (reliability-centered, predictive, time-based preventive, condition-based, opportunistic, and corrective maintenance) are considered to specify the optimal maintenance strategies. As a result of the application, the most appropriate maintenance strategy was emerged as reliability-centered maintenance (RCM). Other alternatives were appeared as predictive (PdM), time-based preventive (TBPM), condition-based (CBM), opportunistic (OM), and corrective maintenance (CM). The validation of the method was performed using the PF-VIKOR technique as a comparative study. In the final stage of the study, a sensitivity analysis based on criteria weights was conducted to test the robustness of the proposed integrated methodology.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The authors confirm that data supporting the findings of this study are available within the article, apart from its supplementary materials.

References

  1. Emovon, I., Norman, R.A., Murphy, A.J.: Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. J Intell Manuf 29(3), 519–531 (2018). https://doi.org/10.1007/s10845-015-1133-6

    Article  Google Scholar 

  2. Bevilacqua, M., Braglia, M.: The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Saf 70(1), 71–83 (2000). https://doi.org/10.1016/S0951-8320(00)00047-8

    Article  Google Scholar 

  3. Wang, L., Chu, J., Wu, J.: Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107(1), 151–163 (2007). https://doi.org/10.1016/j.ijpe.2006.08.005

    Article  Google Scholar 

  4. Cayir Ervural, B., Evren, R., Delen, D.: A multi-objective decision-making approach for sustainable energy investment planning. Renew Energy 126, 387–402 (2018). https://doi.org/10.1016/J.RENENE.2018.03.051

    Article  Google Scholar 

  5. Cayir Ervural, B., Zaim, S., Demirel, O.F., Aydin, Z., Delen, D.: An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renew Sustain Energy Rev 82, 1538–1550 (2018). https://doi.org/10.1016/J.RSER.2017.06.095

    Article  Google Scholar 

  6. Yager, R.R.: Pythagorean fuzzy subsets. In: 2013 Joint IFSA World Congress and NAFIPS annual meeting (IFSA/NAFIPS), Jun. 2013, pp. 57–61. doi: https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375.

  7. Onar, S.C., Oztaysı, B., Kahraman, C.: Multicriteria evaluation of cloud service providers using pythagorean fuzzy TOPSIS. J Multiple-Valued Logic Soft Comput 30(2–3), 263–283 (2018)

    Google Scholar 

  8. Al-Najjar, B., Alsyouf, I.: Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1), 85–100 (2003). https://doi.org/10.1016/S0925-5273(02)00380-8

    Article  Google Scholar 

  9. Kirubakaran, B., Ilangkumaran, M.: Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS. Ann Oper Res 245(1–2), 285–313 (2016). https://doi.org/10.1007/s10479-014-1775-3

    Article  MathSciNet  MATH  Google Scholar 

  10. Ge, Y., Xiao, M., Yang, Z., Zhang, L., Hu, Z., Feng, D.: An integrated logarithmic fuzzy preference programming based methodology for optimum maintenance strategies selection. Appl Soft Comput 60, 591–601 (2017). https://doi.org/10.1016/J.ASOC.2017.07.021

    Article  Google Scholar 

  11. Ighravwe, D.E., Oke, S.A.: A two-stage fuzzy multi-criteria approach for proactive maintenance strategy selection for manufacturing systems. SN Appl Sci 2(10), 1–19 (2020). https://doi.org/10.1007/s42452-020-03484-6

    Article  Google Scholar 

  12. Chan, F.T.S., Prakash, A.: Maintenance policy selection in manufacturing firms using the fuzzy MCDM approach. Int J Prod Res 50(23), 7044–7056 (2012). https://doi.org/10.1080/00207543.2011.653451

    Article  Google Scholar 

  13. Aghaee, A., Aghaee, M., Fathi, M.R., Shoa’bin, S., Sobhani, S.M.: A novel fuzzy hybrid multi-criteria decision-making approach for evaluating maintenance strategies in petrochemical industry. J Qual Maint Eng (2020). https://doi.org/10.1108/JQME-04-2019-0036

    Article  Google Scholar 

  14. Tan, Z., Li, J., Wu, Z., Zheng, J., He, W.: An evaluation of maintenance strategy using risk based inspection. Saf Sci 49(6), 852–860 (2011). https://doi.org/10.1016/J.SSCI.2011.01.015

    Article  Google Scholar 

  15. Gholami, J., Razavi, A., Ghaffarpour, R.: Decision-making regarding the best maintenance strategy for electrical equipment of buildings based on fuzzy analytic hierarchy process; case study: elevator. J Qual Maint Eng (2021). https://doi.org/10.1108/JQME-03-2020-0015/FULL/PDF

    Article  Google Scholar 

  16. Hemmati, N., Rahiminezhad Galankashi, M., Imani, D.M., Mokhatab Rafiei, F.: An integrated fuzzy-AHP and TOPSIS approach for maintenance policy selection. Int J Qual Reliab Manage 37(9–10), 1275–1299 (2019). https://doi.org/10.1108/IJQRM-10-2018-0283

    Article  Google Scholar 

  17. Özcan, E., Ünlüsoy, S., Eren, T.: A combined goal programming—AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renew. Sustain. Energy Rev. 78, 1410–1423 (2017). https://doi.org/10.1016/J.RSER.2017.04.039

    Article  Google Scholar 

  18. KumarSagar, M., Jayaswal, P., Kushwah, K.: Exploring fuzzy SAW method for maintenance strategy selection problem of material handling equipment. Int J Curr Eng Technol 3(2), 600–605 (2013)

    Google Scholar 

  19. Nezami, F.G., Yildirim, M.B.: A sustainability approach for selecting maintenance strategy. Int. J. Sustain. Eng. 6(4), 332–343 (2013). https://doi.org/10.1080/19397038.2013.765928

    Article  Google Scholar 

  20. Kumar, G., Maiti, J.: Modeling risk based maintenance using fuzzy analytic network process. Expert Syst Appl 39(11), 9946–9954 (2012). https://doi.org/10.1016/J.ESWA.2012.01.004

    Article  Google Scholar 

  21. Hemmati, N., Rahiminezhad Galankashi, M., Imani, D.M., Farughi, H.: Maintenance policy selection: a fuzzy-ANP approach. J Manuf Technol Manage 29(7), 1253–1268 (2018). https://doi.org/10.1108/JMTM-06-2017-0109

    Article  Google Scholar 

  22. Shyjith, K., Ilangkumaran, M., Kumanan, S.: Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry. J Qual Maint Eng 14(4), 375–386 (2008). https://doi.org/10.1108/13552510810909975

    Article  Google Scholar 

  23. Ilangkumaran, M., Kumanan, S.: Selection of maintenance policy for textile industry using hybrid multi-criteria decision making approach. J. Manuf. Technol. Manag. 20(7), 1009–1022 (2009). https://doi.org/10.1108/17410380910984258

    Article  Google Scholar 

  24. Panchal, D., Chatterjee, P., Shukla, R.K., Choudhury, T., Tamosaitiene, J.: Integrated fuzzy AHP-CODAS framework for maintenance decision in urea fertilizer industry. Econ Comput Econ Cybern Stud Res 51(3), 179–196 (2017)

    Google Scholar 

  25. Carpitella, S., et al.: A risk evaluation framework for the best maintenance strategy: the case of a marine salt manufacture firm. Reliab Eng Syst Saf 205, 107265 (2021). https://doi.org/10.1016/j.ress.2020.107265

    Article  Google Scholar 

  26. Shafiee, M.: Maintenance strategy selection problem: an MCDM overview. J Qual Maint Eng 21(4), 378–402 (2015). https://doi.org/10.1108/JQME-09-2013-0063

    Article  MathSciNet  Google Scholar 

  27. Vahdani, B., Hadipour, H., Sadaghiani, J.S., Amiri, M.: Extension of VIKOR method based on interval-valued fuzzy sets. Int J Adv Manuf Technol 47(9–12), 1231–1239 (2010). https://doi.org/10.1007/s00170-009-2241-2

    Article  Google Scholar 

  28. Abdelhadi, A.: Maintenance scheduling based on PROMETHEE method in conjunction with group technology philosophy. Int J Qual Reliab Manage 35(7), 1423–1444 (2018). https://doi.org/10.1108/IJQRM-03-2017-0053

    Article  Google Scholar 

  29. Darestani, S.A., Palizban, T., Imannezhad, R.: Maintenance strategy selection: a combined goal programming approach and BWM-TOPSIS for paper production industry. J Qual Maint Eng 28(1), 14–36 (2022). https://doi.org/10.1108/JQME-03-2019-0022

    Article  Google Scholar 

  30. Zhang, X.: Multicriteria Pythagorean fuzzy decision analysis: a hierarchical QUALIFLEX approach with the closeness index-based ranking methods. Inf Sci (NY) 330, 104–124 (2016). https://doi.org/10.1016/J.INS.2015.10.012

    Article  Google Scholar 

  31. Ren, P., Xu, Z., Gou, X.: Pythagorean fuzzy TODIM approach to multi-criteria decision making. Appl Soft Comput 42, 246–259 (2016). https://doi.org/10.1016/J.ASOC.2015.12.020

    Article  Google Scholar 

  32. Akram, M., Dudek, W.A., Ilyas, F.: Group decision-making based on pythagorean fuzzy TOPSIS method. Int. J. Intell. Syst. 34(7), 1455–1475 (2019). https://doi.org/10.1002/int.22103

    Article  Google Scholar 

  33. Ye, J., Chen, T.-Y.: Selection of cotton fabrics using pythagorean fuzzy TOPSIS approach. J Nat Fibers 19(14), 9085–9100 (2022). https://doi.org/10.1080/15440478.2021.1982439

    Article  Google Scholar 

  34. Gedikli, T., Ervural, B.C., Sen, D.T.: Evaluation of maintenance strategies using Pythagorean fuzzy TOPSIS method. Adv Intell Syst Comput 1197, 512–521 (2021). https://doi.org/10.1007/978-3-030-51156-2_59

    Article  Google Scholar 

  35. Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C.: A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf Sci 103, 124–136 (2018). https://doi.org/10.1016/J.SSCI.2017.10.025

    Article  Google Scholar 

  36. Otay, I., Jaller, M.: A novel Pythagorean fuzzy AHP and TOPSIS method for the wind power farm location selection problem. J Intell Fuzzy Syst 39(5), 6193–6204 (2020). https://doi.org/10.3233/JIFS-189089

    Article  Google Scholar 

  37. Cui, F.B., You, X.Y., Shi, H., Liu, H.C.: Optimal siting of electric vehicle charging stations using Pythagorean fuzzy VIKOR approach. Math Probl Eng 2018, 1–12 (2018). https://doi.org/10.1155/2018/9262067

    Article  Google Scholar 

  38. Liang, D., Xu, Z.: The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Appl Soft Comput 60, 167–179 (2017). https://doi.org/10.1016/J.ASOC.2017.06.034

    Article  Google Scholar 

  39. Çalık, A.: A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput 25(3), 2253–2265 (2021). https://doi.org/10.1007/S00500-020-05294-9

    Article  Google Scholar 

  40. Kaya, İ, Karaşan, A., Özkan, B., Çolak, M.: An integrated decision-making methodology based on Pythagorean fuzzy sets for social robot evaluation. Soft Comput 26(19), 9831–9858 (2022). https://doi.org/10.1007/S00500-022-07303-5/TABLES/36

    Article  Google Scholar 

  41. Gedikli, T., CayirErvural, B.: Identification of optimum COVID-19 vaccine distribution strategy under integrated Pythagorean fuzzy environment. Lecture Notes Mech Eng (2022). https://doi.org/10.1007/978-3-030-90421-0_6/COVER

    Article  Google Scholar 

  42. Ren, Z., Verma, A.S., Li, Y., Teuwen, J.J.E., Jiang, Z.: Offshore wind turbine operations and maintenance: a state-of-the-art review. Renew Sustain Energy Rev 144, 110886 (2021). https://doi.org/10.1016/J.RSER.2021.110886

    Article  Google Scholar 

  43. Asuquo, M.P., Wang, J., Zhang, L., Phylip-Jones, G.: Application of a multiple attribute group decision making (MAGDM) model for selecting appropriate maintenance strategy for marine and offshore machinery operations. Ocean Eng. 179, 246–260 (2019). https://doi.org/10.1016/J.OCEANENG.2019.02.065

    Article  Google Scholar 

  44. Zhang, X., Xu, Z.: Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. Int. J. Intell. Syst. 29(12), 1061–1078 (2014). https://doi.org/10.1002/int.21676

    Article  MathSciNet  Google Scholar 

  45. Peng, X., Yang, Y.: Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators. Int. J. Intell. Syst. 31(5), 444–487 (2016). https://doi.org/10.1002/int.21790

    Article  Google Scholar 

  46. Zhang, X.: A novel approach based on similarity measure for Pythagorean fuzzy multiple criteria group decision making. Int. J. Intell. Syst. 31(6), 593–611 (2016). https://doi.org/10.1002/int.21796

    Article  Google Scholar 

  47. Cayir Ervural, B., Ervural, B., Kabak, Ö.: Evaluation of flexible manufacturing systems using a hesitant group decision making approach. J Intell Syst 28(2), 245–258 (2019). https://doi.org/10.1515/jisys-2017-0065

    Article  Google Scholar 

  48. Büyüközkan, G., Çifçi, G.: A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl 39(3), 2341–2354 (2012). https://doi.org/10.1016/J.ESWA.2011.08.061

    Article  Google Scholar 

  49. Srivastava, P., Khanduja, D., Agrawal, V.P.: Agile maintenance attribute coding and evaluation based decision making in sugar manufacturing plant. Opsearch 57(2), 553–583 (2020). https://doi.org/10.1007/s12597-019-00426-8

    Article  MATH  Google Scholar 

  50. Arjomandi, M.A., Dinmohammadi, F., Mosallanezhad, B., Shafiee, M.: A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets. Adv. Mech. Eng. 13(4), 1–21 (2021). https://doi.org/10.1177/1687814021994965/ASSET/IMAGES/LARGE/10.1177_1687814021994965-FIG2.JPEG

    Article  Google Scholar 

  51. Fathi, M.R., Momeni, M., Zarchi, M.K., Azizollahi, S.: A fuzzy TOPSIS-based approach to maintenance strategy selection: a case study. Middle-East J. Sci. Res. 8(3), 699–706 (2011)

    Google Scholar 

  52. Seiti, H., Tagipour, R., Hafezalkotob, A., Asgari, F.: Maintenance strategy selection with risky evaluations using RAHP. J Multi-Criteria Decision Analys 24(5–6), 257–274 (2017). https://doi.org/10.1002/mcda.1618

    Article  Google Scholar 

  53. Kurian, M.C., Shalij, P.R., Pramod, V.R.: Maintenance strategy selection in a cement industry using analytic network process. J Qual Maint Eng 26(4), 509–525 (2019). https://doi.org/10.1108/JQME-07-2017-0048

    Article  Google Scholar 

  54. Gedikli, T., Cayir Ervural, B.: Selection optimum maintenance strategy using multi-criteria decision making approaches. In: Calısır, F., Korhan, O. (eds.) Industrial engineering in the digital disruption era, pp. 156–170. Springer (2019)

    Google Scholar 

  55. Chopra, A., Sachdeva, A., Bhardwaj, A.: Selection of appropriate maintenance strategy using fuzzy VIKOR technique: application in paper industry. Int J Qual Reliab Manage. (2021). https://doi.org/10.1108/IJQRM-03-2020-0070

    Article  Google Scholar 

  56. Li, H., Yazdi, M., Huang, C.G., Peng, W.: A reliable probabilistic risk-based decision-making method: Bayesian technique for order of preference by similarity to ideal solution (B-TOPSIS). Soft Comput 26(22), 12137–12153 (2022). https://doi.org/10.1007/S00500-022-07462-5/TABLES/4

    Article  Google Scholar 

  57. Kareem, A., Jawwad, A., Amman, J., Abunaffa, I.: Applying analytical hierarchy process (AHP) in selecting best maintenance strategies for newly established chemical fertilizers plants. J Qual Maint Eng 28(3), 545–566 (2022). https://doi.org/10.1108/JQME-06-2020-0056

    Article  Google Scholar 

  58. Bertolini, M., Bevilacqua, M., Braglia, M., Frosolini, M.: An analytical method for maintenance outsourcing service selection. Int J Qual Reliab Manage 21(7), 772–788 (2004). https://doi.org/10.1108/02656710410549118

    Article  Google Scholar 

  59. Xie, H., Shi, L., Xu, H.: Transformer maintenance policies selection based on an improved fuzzy analytic hierarchy process. J Comput (Taipei) 8(5), 1343–1350 (2013). https://doi.org/10.4304/jcp.8.5.1343-1350

    Article  Google Scholar 

  60. Abdulgader, F.S., Eid, R., Rouyendegh, B.D.: Development of decision support model for selecting a maintenance plan using a fuzzy MCDM approach: a theoretical framework. Appl Comput Intell Soft Comput 14, 1–14 (2018). https://doi.org/10.1155/2018/9346945

    Article  Google Scholar 

  61. Pourjavad, E., Shirouyehzad, H., Shahin, A.: Selecting maintenance strategy in mining industry by analytic network process and TOPSIS. Int. J. Ind. Syst. Eng. 15(2), 171 (2013). https://doi.org/10.1504/ijise.2013.056095

    Article  Google Scholar 

  62. Verma, A.K., Srividya, A., Gaonkar, R.S.P.: Fuzzy set solutions for optimal maintenance strategy selection. Opsearch 44(3), 261–276 (2007). https://doi.org/10.1007/bf03399213

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Beyzanur Cayir Ervural.

Appendix A

Appendix A

Detailed data of fuzzy comparison matrices.

See Tables

Table 13 Pairwise comparison of main criteria with respect to experts’ opinions

13 and

Table 14 Pairwise comparison of sub-criteria under safety, cost, reliability, feasibility, and added-value criteria with respect to experts’ opinions

14.

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

Gedikli, T., Ervural, B.C. Evaluation of Maintenance Policies Using a Two-Stage Pythagorean-Based Group Decision-Making Approach. Int. J. Fuzzy Syst. 25, 1795–1817 (2023). https://doi.org/10.1007/s40815-023-01476-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01476-3

Keywords

Navigation