Maintenance policy selection considering resilience engineering by a new interval-valued fuzzy decision model under uncertain conditions

Document Type : Article

Authors

1 Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

2 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

3 Faculty of Built Environment, University of New South Wales, Sydney, Australia

Abstract

Different maintenance policies, including preventive maintenance and predictive maintenance, are introduced to enhance the execution of systems. Maintenance professional experts have faced numerous challenges with distinguishing the proper maintenance policy, among which causes of failure, accessibility, and the capability of maintenance should be regarded seriously. Moreover, most organizations do not have a deliberate and compelling model for evaluating maintenance policies under uncertainty to deal with real-world conditions. The aim of this paper is to introduce a new interval-valued fuzzy (IVF) decision model for the selection of maintenance policy based on order inclination with comparability to ideal solutions by Monte Carlo simulation. This paper introduces novel separation measures and a new IVF-distinguish index via possibilistic statistical concepts (PSCs) which can assist maintenance decision makers to rank maintenance policy candidates. Also, resilience engineering (RE) factors are considered along with conventional evaluation criteria. Finally, the steps of the proposed IVF model-based PSCs are applied to survey a real case in manufacturing industry. Results of the presented model are compared with the recent literature and could help maintenance personnel in identifying the best policy systematically.

Keywords

References

  1. References:

    1. Ratnayake, R. C., and Markeset, T. “Implementing company policies in plant level asset operations: measuring organisational alignment”, European Journal of Industrial Engineering, 4(3), pp. 355-371 (2010).
    2. Dahane, M., Rezg, N., and Clementz, C. “Subcontracting integration in a joint maintenance/production policy: study on profitability conditions”, European Journal of Industrial Engineering, 5(4), pp. 412-423 (2011).
    3. Nguyen, T. A. T., and Chou, S. Y. “Maintenance strategy selection for improving cost-effectiveness of offshore wind systems”, Energy Conversion and Management, 157, pp. 86-95 (2018).
    4. Lai, X., Chen, Z., and Bidanda, B. “Optimal decision of an economic production quantity model for imperfect manufacturing under hybrid maintenance policy with shortages and partial backlogging”, International Journal of Production Research, pp. 1-25 (2019). https://doi.org/10.1080/00207543.2018.1562249.
    5. Wang, Y. M., and Elhag, T. M. “Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment”, Expert systems with applications, 31(2), pp. 309-319 (2006).
    6. Panagiotidou, S., and Tagaras, G. “Evaluation of maintenance policies for equipment subject to quality shifts and failures”, International Journal of Production Research, 46(20), pp. 5761-5779 (2008).
    7. Al-Najjar, B., and Alsyouf, I. “Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making”, International journal of production economics, 84(1), pp. 85-100 (2003).
    8. Azadivar, F., and Shu, V. “Maintenance policy selection for JIT production systems”, International Journal of Production Research, 37(16), pp. 3725-3738 (1999).
    9. Alborzi, S., Aminian, A., Mojtahedi, S. M. H., and Mousavi, S.M. “An analysis of project risks using the non-parametric bootstrap technique”. In 2008 IEEE International Conference on Industrial Engineering and Engineering Management 1295-1299 (2008, December).
    10. Pascual, R., and Ortega, J. H. “Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts”, Reliability Engineering & System Safety, 91(2), pp. 241-248 (2006).
    11. Bartholomew-Biggs, M., Zuo, M. J., and Li, X. “Modelling and optimizing sequential imperfect preventive maintenance”, Reliability Engineering & System Safety, 94(1), pp. 53-62 (2009).
    12. Yuniarto, M. N., and Labib, A. W. “Fuzzy adaptive preventive maintenance in a manufacturing control system: a step towards self-maintenance”, International Journal of Production Research, 44(1), pp. 159-180 (2006).
    13. Marmier, F., Varnier, C., and Zerhouni, N. “Proactive, dynamic and multi-criteria scheduling of maintenance activities”, International Journal of Production Research, 47(8), pp. 2185-2201 (2009).
    14. Davoudabadi, R., Mousavi, S.M., Mohagheghi, V., and Vahdani, B. “Resilient supplier selection through introducing a new interval-valued intuitionistic fuzzy evaluation and decision-making framework”, Arabian Journal for Science and Engineering, 44(8), pp. 7351-7360 (2019).
    15. Davoudabadi, R., Mousavi, S.M., Šaparauskas, J., and Gitinavard, H. “Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach”, Journal of Civil Engineering and Management,25(3), pp. 241-251 (2019).
    16. Mousavi, S.M. “A new interval-valued hesitant fuzzy pairwise comparison–compromise solution methodology: an application to cross-docking location planning”, Neural Computing and Applications, 31(9), pp. 5159-5173 (2019).
    17. Gitinavard, H., Foroozesh, N., Mousavi, S.M., and Mohagheghi, V. “Soft computing based on a selection index method with risk preferences under uncertainty: applications to construction industry”, International Journal of Computational Systems Engineering,4(4), pp. 238-247 (2018).
    18. Desai, S., Bidanda, B., and Lovell, M.R. “Material and process selection in product design using decision-making technique (AHP)”, European Journal of Industrial Engineering, 6(3), pp. 322-346 (2012).
    19. Foroozesh, N., Tavakkoli-Moghaddam, R., and Mousavi, S.M. “Sustainable-supplier selection for manufacturing services: a failure mode and effects analysis model based on interval-valued fuzzy group decision-making”, International Journal of Advanced Manufacturing Technology, 95(9-12), pp. 3609-3629 (2018).
    20. Labib, A. W., O'Connor, R. F., and Williams, G. B. “An effective maintenance system using the analytic hierarchy process”, Integrated Manufacturing Systems, 9(2), pp. 87-98 (1998).
    21. Bevilacqua, M., and Braglia, M. “The analytic hierarchy process applied to maintenance strategy selection”, Reliability Engineering & System Safety, 70(1), pp. 71-83 (2000).
    22. Dey, P.K. “Decision support system for inspection and maintenance: a case study of oil pipelines”, IEEE Transactions on Engineering Management, 51(1), pp. 47-56 (2004)
    23. Shyjith, K., Ilangkumaran, M., and Kumanan, S. “Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry”, Journal of Quality in Maintenance Engineering, 14(4), pp. 375-386 (2008).
    24. Chatterjee, K., and Kar, S. “Unified Granular-number-based AHP-VIKOR multi-criteria decision framework”, Granular Computing, 2(3), pp.199-221 (2017).
    25. Chatterjee, K., Kar, M.B., and Kar, S. “Strategic decisions using Intuitionistic fuzzy VIKOR method for Information System (IS) outsourcing”, In 2013 IEEE International Symposium on Computational and Business Intelligence, 123-126 (2013, August).
    26. Das, S., and Kar, S. “Group decision making in medical system: An intuitionistic fuzzy soft set approach”, Applied soft computing, 24, pp.196-211 (2014).
    27. Das, S., Ghosh, P. K., and Kar, S. “Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system”, In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-7 (2013, July).
    28. Das, S., Kar, S., and Pal, T. “Robust decision making using intuitionistic fuzzy numbers”, Granular Computing, 2(1), pp. 41-54 (2017).
    29. Bertolini, M., and Bevilacqua, M. “A combined goal programming—AHP approach to maintenance selection problem”. Reliability Engineering & System Safety, 91(7), pp. 839-848 (2006).
    30. Arunraj, N. S., and Maiti, J. “Risk-based maintenance policy selection using AHP and goal programming”, Safety science, 48(2), pp. 238-247 (2010).
    31. Triantaphyllou, E., Kovalerchuk, B., Mann, L., and Knapp, G. M. “Determining the most important criteria in maintenance decision making”, Journal of Quality in Maintenance Engineering, 3(1), pp. 16-28 (1997).
    32. Luce, S. “Choice criteria in conditional preventive maintenance”, Mechanical Systems and Signal Processing, 13(1), pp. 163-168 (1999).
    33. Murthy, D. N. P., and Asgharizadeh, E. “Optimal decision making in a maintenance service operation”, European Journal of Operational Research, 116(2), pp. 259-273 (1999).
    34. Tahir, Z., Prabuwono, A. S., and Aboobaider, B. M. “Maintenance decision support system in small and medium industries: an approach to new optimization model”, International Journal of Computer Science and Network Security, 8(11), pp. 155-162 (2008).
    35. Sachdeva, A., Kumar, D., and Kumar, P. “A methodology to determine maintenance criticality using AHP”. International Journal of Productivity and Quality Management, 3(4), pp. 396-412 (2008).
    36. Kodali, R., Prasad Mishra, R., and Anand, G. “Justification of world-class maintenance systems using analytic hierarchy constant sum method”, Journal of Quality in Maintenance Engineering, 15(1), pp. 47-77 (2009).
    37. Meselhy, K. T., ElMaraghy, W. H., and ElMaraghy, H. A. “A periodicity metric for assessing maintenance strategies”, CIRP Journal of Manufacturing Science and Technology, 3(2), pp. 135-141 (2010).
    38. Labib, A. W. “A decision analysis model for maintenance policy selection using a CMMS”, Journal of Quality in Maintenance Engineering, 10(3), pp. 191-202 (2004).
    39. Ghosh, D., and Roy, S. “A decision-making framework for process plant maintenance”, European Journal of Industrial Engineering, 4(1), pp. 78-98 (2009).
    40. Pariazar, M., Shahrabi, J., Zaeri, M. S., and Parhizi, S. “A combined approach for maintenance strategy selection”, Journal of Applied Sciences, 8(23), pp. 4321-4329 (2008).
    41. Chan, F. T., and Prakash, A. “Maintenance policy selection in manufacturing firms using the fuzzy MCDM approach”, International Journal of Production Research, 50(23), pp. 7044-7056 (2012).
    42. Jafari, A., Jafarian, M., Zareei, A., and Zaerpour, F. “Using fuzzy Delphi method in maintenance strategy selection problem”, Journal of Uncertain Systems, 2(4), pp. 289-298 (2008).
    43. Li, C., Xu, M., and Guo, S. “ELECTRE III based on ranking fuzzy numbers for deterministic and fuzzy maintenance strategy decision problems”, In IEEE International Conference on Automation and Logistics, pp. 309-312 (2007, August).
    44. Pourjavad, E., Shirouyehzad, H., and Shahin, A. “Selecting maintenance strategy in mining industry by analytic network process and TOPSIS”, International Journal of Industrial and Systems Engineering, 15(2), pp. 171-192 (2013).
    45. Ding, S. H., and Kamaruddin, S. “Maintenance policy optimization—literature review and directions”, International Journal of Advanced Manufacturing Technology, 76(5-8), pp. 1263-1283 (2015).
    46. Ding, S. H., Kamaruddin, S., and Azid, I. A. “Development of a model for optimal maintenance policy selection”, European Journal of Industrial Engineering, 8(1), pp. 50-68 (2014).
    47. Nazeri, A., and Naderikia, R. “A new fuzzy approach to identify the critical risk factors in maintenance management”, International Journal of Advanced Manufacturing Technology, 92(9-12), pp. 3749-3783 (2017).
    48. Mechefske, C. K., and Wang, Z. “Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies”, Mechanical Systems and Signal Processing, 17(2), pp. 305-316 (2003).
    49. Sugumaran, C., Muthu, S., Devadasan, S. R., Pramod, V. R., and Srinivasan, K. “From TPM to analytic maintenance quality function deployment: a literature journey via QFD and AHP”, International Journal of Indian Culture and Business Management, 4(4), pp. 390-418 (2011).
    50. Maletič, D., Maletič, M., Lovrenčić, V., Al-Najjar, B., and Gomišček, B. “An application of analytic hierarchy process (AHP) and sensitivity analysis for maintenance policy selection”, Organizacija, 47(3), pp. 177-188 (2014).
    51. Rahimi, M., Sadinejad, S., and Khalili-Damghani, K. “Selecting the most appropriate maintenance strategies using fuzzy analytic network process: a case study of Saipa vehicle industry”, Decision Science Letters, 3(2), pp. 237-242 (2014).
    52. Azadeh, A., and Abdolhossein Zadeh, S. “An integrated fuzzy analytic hierarchy process and fuzzy multiple-criteria decision-making simulation approach for maintenance policy selection”. Simulation, 92(1), pp. 3-18 (2016).
    53. Hemmati, N., Galankashi, M. R., Imani, D. M., and Rafiei, F. M. “An integrated fuzzy-AHP and TOPSIS approach for maintenance policy selection”, International Journal of Quality & Reliability Management, (2019). https://doi.org/10.1108/IJQRM-10-2018-0283.
    54. Khalaj, M., Tavakkoli-Moghaddam, R., Khalaj, F., and Siadat, A. “New definition of the cross entropy based on the Dempster-Shafer theory and its application in a decision-making process”, Communications in Statistics-Theory and Methods, 49(4), pp. 909-923 (2020).
    55. Mokhtarian, M. N., Sadi-nezhad, S., and Makui, A. “A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill”, Computers & Industrial Engineering, 78, pp.213-233 (2014).
    56. Ashtiani, B., Haghighirad, F., Makui, A., and ali Montazer, G. “Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets”, Applied Soft Computing, 9(2), pp. 457-461 (2009).
    57. Zhang, W. G., Wang, Y. L., Chen, Z. P., and Nie, Z. K. “Possibilistic mean–variance models and efficient frontiers for portfolio selection problem”, Information Sciences, 177(13), pp. 2787-2801 (2007).
    58. Ye, F., and Lin, Q. “Partner Selection in a Virtual Enterprise: A Group Multiattribute Decision Model with Weighted Possibilistic Mean Values”, Mathematical Problems in Engineering, 2013 (2013).
    59. Deng, X., and Li, R. “Gradually tolerant constraint method for fuzzy portfolio based on possibility theory”, Information Sciences, 259, pp. 16-24 (2014).
    60. Foroozesh, N., Tavakkoli-Moghaddam, R., Mousavi, S. M., and Vahdani, B. “Dispatching rule evaluation in flexible manufacturing systems by a new fuzzy decision model with possibilistic-statistical uncertainties”, Arabian Journal for Science and Engineering, 42(7), pp. 2947-2960 (2017).
    61. Dubois, D., Lang, J., and Prade, H. “Theorem proving under uncertainty: a possibility theory-based approach”, In Proceedings of the 10th international joint conference on Artificial Intelligence-Volume 2, pp. 984-986 (1987, August).
    62. Goetschel Jr, R., and Voxman, W. “Elementary fuzzy calculus”, Fuzzy sets and systems. 18(1), pp. 31-43 (1986).
    63. Al-Najjar, B. “Condition-Based Maintenance: Selection and Improvement of a Cost-Effective Vibration-Based Maintenance Policy for Rolling element Bearings”. Lund University (1997).
    64. Kelly, A., Maintenance strategy. Elsevier (1997).
    65. Chen, D., and Trivedi, K. S. “Closed-form analytical results for condition-based maintenance”. Reliability Engineering & System Safety, 76(1), pp. 43-51(2002).
    66. Samrout, M., Châtelet, E., Kouta, R., and Chebbo, N. “Optimization of maintenance policy using the proportional hazard model”, Reliability Engineering & System Safety, 94(1), pp. 44-52 (2009).
    67. Selim, M., and Gurel, A. S. “Machining conditions-based preventive maintenance”, International Journal of Production Research, 45(8), pp. 1725-1743 (2007).
    68. Bamber, C. J., Sharp, J. M., and Hides, M. T. “Factors affecting successful implementation of total productive maintenance: a UK manufacturing case study perspective”, Journal of Quality in Maintenance Engineering, 5(3), pp. 162-181(1999).
    69. Grall, A., Dieulle, L., Bérenguer, C., and Roussignol, M. “Continuous-time predictive-maintenance scheduling for a deteriorating system”, Reliability, IEEE Transactions on, 51(2), pp. 141-150 (2002).
    70. Willmott, P. “Total productive maintenance: The Western way”, Butterworth-Heinemann (1997).
    71. Sherwin, D. “A review of overall models for maintenance management”, Journal of Quality in Maintenance Engineering, 6(3), pp. 138-164 (2000).
    72. Crocker, J., and Kumar, U. D. “Age-related maintenance versus reliability centred maintenance: a case study on aero-engines”, Reliability Engineering & System Safety, 67(2), 113-118 (2000).
    73. Azadivar, F. “Simulation optimization methodologies”. In Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future-Volume 1, pp. 93-100. ACM (1999, December).
    74. Goldsman, D., Nelson, B. L., Opicka, T., and Pritsker, A. B. “A ranking and selection project: experiences from a university-industry collaboration”, In Proceedings of the 31st conference on Winter simulation: Simulation-a bridge to the future-Volume 1, pp. 83-92. ACM (1999, December).
    75. Au-Yong, C. P., Ali, A. S., and AhmAd, F. “Prediction cost maintenance model of office building based on condition-based maintenance”, EksploatacjaiNiezawodnosc-Maintenance and Reliability, 16(2), 319-324 (2014).
    76. Chen, T. Y. “Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints”, Expert Systems with Applications, 39(2), pp. 1848-1861 (2012).
Scientia Iranica
Volume 29, Issue 2
Transactions on Industrial Engineering (E)
March and April 2022
Pages 783-799

Files

Share

How to cite

Statistics