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Bakım Stratejisi Seçimi için Bulanık PIPRECIA ve Bulanık MOORA Yöntemlerinin Entegrasyonu

Year 2023, Volume: 10 Issue: 2, 401 - 423, 30.06.2023
https://doi.org/10.47097/piar.1256081

Abstract

Günümüz rekabet ortamında firmalar üzerinde zamanında teslimat sağlayarak maliyetleri düşürme ve kaliteyi artırma baskısı bulunmaktadır. Bakım, maliyetlerinin düşürülmesinde, kalitenin yükseltilmesinde, arızaların azaltılmasında, makine duruş sürelerinin en aza indirilmesinde, verimliliğin artırılmasında ve bunun sonucunda işletmelerin hedeflerine ulaşmasında önemli bir rol oynamaktadır. Bu makalenin amacı, bütünleşik bulanık ÇKKV (Çok Kriterli Karar Verme) yaklaşımı kullanarak bir üretim şirketi için en iyi bakım stratejisini seçmektir. Bu yaklaşım bulanık PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) ve bulanık MOORA (Multi Objective and Optimization on the Basis of Ratio Analysis) yöntemlerine dayanmaktadır. Bakım stratejisinin seçimi, Çok Kriterli Bir Karar Verme (ÇKKV) problemidir. Bu problem, alternatifleri ve kriterleri kesin ifadelerle değerlendirmede belirsizlikler ve zorluklar içerdiğinden, en iyi bakım stratejisini seçmek için bulanık ÇKKV yaklaşımı önerilmiştir. Önerilen entegre yöntemin üretim işletmesinde uygulanması sonucunda bakım stratejilerinin sıralaması elde edilmiş ve kestirimci bakım stratejisinin firma için en uygun bakım stratejisi olduğu belirlenmiştir.

References

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  • Almedia, A.T., & Bohoris, G.A. (1995). Decision theory in maintenance decision making. Journal of Quality in Maintenance Engineering, 1(1), 39–45.
  • Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using fuzzy multiple criteria decision-making. International Journal of Production Economics, 84, 85–100.
  • Arabsheybani, A., Paydar, M. M., & Safaei A. S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk. Journal of Cleaner Production, 190, 577-591.
  • Archana, M., & Sujatha, V. (2012). Application of fuzzy MOORA and GRA in multi-criterion decision making problems. International Journal of Computer Applications, 53(9), 46-50.
  • Arjomandi, M. A., Dinmohammadi, F., Mosallanezhad, B., & Shafiee, M. (2021). A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets. Advances in Mechanical Engineering, 13(4), 1–21.
  • Arman, K. ve Kundakcı, N. (2022). Bulanık PIPRECIA yöntemi ile bankacılık endüstrisinde blokzincir teknolojisinin benimsenmesini etkileyen kritik faktörlerin değerlendirilmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25 (47), 79-92, doi: 10.31795/baunsobed.975891.
  • Azadivar, F., & Shu, V. (1999). Maintenance policy selection for JIT production systems. International Journal of Production Research, 37(16), 3725-3738, doi:10.1080/002075499190013.
  • Bakhat, R., & Rajaa, M. (2020). Development of a fuzzy hybrid approach for solving the maintenance strategy selection problem in macro systems. Strategy Management Logistics, ISSN:2509-0186.
  • Baležentis, A., Baležentis, T., & Brauers, W. K. M. (2012). Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Systems with Applications, 39(9), 7961–7967.
  • Bashiri, M., Badri, H., & Hejazi, T. H. (2011). Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Applied Mathematical Modelling, 35, 152–164.
  • Bera, A.K, Jana D. K., Banerjee, D., & Nandy, T. (2020). Supplier selection using extended IT2 fuzzy TOPSIS and IT2 fuzzy MOORA considering subjective and objective factors. Soft Computing, 24, 8899–8915.
  • Bertolini, M., & Bevilacqua, M. (2006). A combined goal programming—AHP approach to maintenance selection problem. Reliability Engineering and System Safety, 91(7), 839–848.
  • Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering and System Safety, 70, 71–83.
  • Blagojević, A., Stević, Ž., Marinković, D., Kasalica, S., & Rajilić, S. (2020). A novel Entropy-fuzzy PIPRECIA-DEA model for safety evaluation of railway traffic. Symmetry, 12, 1479, doi:10.3390/sym12091479.
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  • Carpitella, S., Mzougui, I., Benítez, J., Carpitella, F., Certa, A., Izquierdo, J., & Cascia, M. L. (2021). A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm. Reliability Engineering and System Safety, 205, 107265.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.
  • Cheng, Y.-H., & Tsao, H-L. (2010). Rolling stock maintenance strategy selection, sparesparts’ estimation, and replacements’ interval calculation. International Journal of Production Economics, 128, 404–412.
  • Đalić, I., Stević, Ž., Karamasa, C., & Puška, A. (2020a). A novel integrated fuzzy PIPRECIA–interval rough SAW model: green supplier selection. Decision Making. Applications in Management and Engineering, 3(1), 126-145.
  • Đalić, I., Ateljević, J., Stević, Ž., & Terzić, S. (2020b). An integrated Swot – fuzzy PIPRECIA model for analysis of competitiveness in order to improve logistics performances. Facta Universitatis Series: Mechanical Engineering, 18(3), 439-451.
  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. (2012). A MOORA based fuzzy multi-criteria decision-making approach for supply chain strategy selection. International Journal of Industrial Engineering Computations, 3, 649–662.
  • Dobrosavljević, A., Urošević, S., Vuković, M., Talijan, M., & Marinković, D. (2020). Evaluation of process orientation dimensions in the apparel industry. Sustainability, 12, 4145, doi:10.3390/su12104145.
  • Emovon, I., Norman, R.A., & Murphy, A.J. (2018). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29, 519–531.
  • Emovon, I., Okpako, O. S., & Edjokpa E. (2021). Application of fuzzy MOORA method in the design and fabrication of an automated hammering machine. World Journal of Engineering, 18(1), 37–49.
  • Ersöz, F., Kinci, C.H., & Ersöz, T. (2018). Model proposal for course selection with the fuzzy MOORA approach. European Journal of Science and Technology, 14, 369-377.
  • Gholami, J., Razavi, A., & Ghaffarpour, R. (2022). Decision-making regarding the best maintenance strategy for electrical equipment of buildings based on fuzzy analytic hierarchy process; case study: elevator. Journal of Quality in Maintenance Engineering, 28(3), 652-667.
  • Görener, A. (2013). Maintenance strategy selection by using WSA and TOPSIS methods under fuzzy decision environment. Journal of Engineering and Natural Sciences Sigma, 31, 159-177.
  • Ierace, S. & Cavalieri, S. (2008). Maintenance strategy selection: a comparison between fuzzy logic and analytic hierarchy process. 9th IFAC Workshop on Intelligent Manufacturing Systems. Szczecin, Poland, pp. 228-233.
  • Ighravwe, D. E., & Oke, S.A. (2020). A two‑stage fuzzy multi‑criteria approach for proactive maintenance strategy selection for manufacturing systems. N Applied Sciences, 2, 1683, doi:10.1007/s42452-020-03484-6.
  • Jafari, A., Jafarian, M. Zareei, A., & Zaerpour, F. (2008). Using fuzzy Delphi method in maintenance strategy selection problem. Journal of Uncertain Systems, 2(4), 289–298.
  • Jiménez, J. J. M., Vingerhoeds, R., Grabot, B., & Schwartz, S. (2021). An ontology model for maintenance strategy selection and assessment. Journal of Intelligent Manufacturing, doi: 10.1007/s10845-021-01855-3.
  • Jocic, K. J., Jocic, G., Karabasevic, D., Popovic, G., Stanujkic, D., Zavadskas E. K., & Nguyen, P.T. (2020). A novel integrated PIPRECIA–interval-valued triangular fuzzy ARAS model: e-learning course selection. Symmetry, 12, 928, doi:10.3390/sym12060928.
  • Karande, P., & Chakraborty, S. (2012). A fuzzy-MOORA approach for ERP system selection. Decision Science Letters, 1, 11–22.
  • Khorshidi, M., Erkayman, B., Albayrak, Ö. Kılıç, R., & Demir, H.İ. (2022). Solar power plant location selection using integrated fuzzy DEMATEL and fuzzy MOORA method. International Journal of Ambient Energy, doi: 10.1080/01430750.2022.2068067.
  • Lopez, J. C., & Kolios, A. (2022). Risk-based maintenance strategy selection for wind turbine composite Blades. Energy Reports, 8, 5541–5561.
  • Mandal, U. K., & Sarkar, B. (2012). Selection of best intelligent manufacturing system (IMS) under fuzzy MOORA conflicting MCDM environment. International Journal of Emerging Technology and Advanced Engineering, 2(9), 301-310.
  • Matawale, C. R., Datta, S., Mahapatra, S.S. (2016). Supplier selection in agile supply chain: Application potential of FMLMCDM approach in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA. Benchmarking: An International Journal, 23(7), 2027-2060.
  • Mavi, R. K., Goh, M., & Zarbakhshnia, N. (2017). Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. The International Journal of Advanced Manufacturing Technology, 91, 2401–2418.
  • Mechefske, C.K., & Wang, Z. (2001). Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies. Mechanical Systems and Signal Processing, 17(2), 305–316.
  • Memiş, S., Demir, E., Karamaşa, Ç., & Korucuk, S. (2020). Prioritization of road transportation risks: an application in Giresun province, Operational Research in Engineering Sciences: Theory and Applications, 3(2), 111-126.
  • Momeni, M., Fathi, M. R., Zarchi, M. K., & Azizollahi, S. (2011). A fuzzy TOPSIS-based approach to maintenance strategy selection: a case study. Middle-East Journal of Scientific Research, 8(3), 699-706.
  • Özdağoğlu, A. Keleş, M. K., & Işıldak, B. (2021). Evaluation of the world's busiest airports with PIPRECIA-E, SMART and MARCOS methods. Erciyes University Journal of Faculty of Economics and Business Administrative Sciences, 58, 333-352.
  • Pariazar, M., Shahrabi, J., Zaeri, M.S., & Parhizi, S. (2008). A combined approach for maintenance strategy selection. Journal of Applied Sciences, 8(23), 4321-4329.
  • Patil, A., Soni, G., Prakash, A., & Karwasra, K. (2022). Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches. International Journal of Quality & Reliability Management, 39(3), 675-703.
  • Pérez-Domínguez, L., Alvarado-Iniesta, A., Rodríguez-Borbón, I. & Vergara-Villegas, O. (2015). Intuitionistic fuzzy MOORA for supplier selection. DYNA, 82(191), 34-41.
  • Shafiee, M. (2015). Maintenance strategy selection problem: an MCDM overview. Journal of Quality in Maintenance Engineering, 21(4), 378-402.
  • Stanujkic, D., Kazimieras Zavadskas, E., Karabasevic, D., Smarandache, F. & Turskis, Z., (2017). The use of the pivot pairwise relative criteria importance assessment method for determining the weights of criteria, Romanian Journal of Economics, 20(4), 116-133.
  • Stevenson, W. J. (2007). Operations Management, New York: McGraw-Hill/Irwin.
  • Stević, Ž., Stjepanović, Ž., Božićković, Z. Das, D. K., & Stanujkić, D. (2018). Assessment of conditions for implementing information technology in a warehouse system: a novel fuzzy PIPRECIA method. Symmetry, 10, 586, doi:10.3390/sym10110586.
  • Tomašević, M., Lapuh, L., Stević, Ž., Stanujkić, D., & Karabašević, D. (2020). Evaluation of criteria for the implementation of High-Performance Computing (HPC) in Danube region countries using fuzzy PIPRECIA method. Sustainability, 12, 3017, doi:10.3390/su12073017.
  • Triantaphyllou, E. Kovalerchuk, B. Mann, L., & Knapp, G., (1997). Determining the most important criteria in maintenance decision making. Journal of Quality in Maintenance Engineering, 3(1), 16–28.
  • Tuyet, N.T.A., & Chou, S.-Y. (2018). Maintenance strategy selection for improving cost-effectiveness of offshore wind systems. Energy Conversion and Management, 157, 86–95.
  • Vesković, S., Milinković, S., Abramović, B., & Ljubaj, I. (2020a). Determining criteria significance in selecting reach stackers by applying the fuzzy PIPRECIA method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 72-88.
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Integration of Fuzzy PIPRECIA and Fuzzy MOORA Methods for Maintenance Strategy Selection

Year 2023, Volume: 10 Issue: 2, 401 - 423, 30.06.2023
https://doi.org/10.47097/piar.1256081

Abstract

In today’s competitive environment, there is a pressure on companies for reducing costs and increasing the quality by providing on time delivery. Maintenance, plays an important role in reducing cost, improving quality, reducing failures, minimizing machine downtime, increasing productivity and as a result achieving objectives of company. The aim of this paper is to select best maintenance strategy for a manufacturing company by using an integrated fuzzy MCDM (Multi-Criteria Decision Making) approach. This approach is based on fuzzy PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) and fuzzy MOORA (Multi Objective and Optimization on the Basis of Ratio Analysis) methods. The selection of maintenance strategy is a multi-criteria decision making (MCDM) problem. As this problem includes uncertainties and difficulty in evaluating alternatives and criteria with definite expressions, fuzzy MCDM approach is proposed for selecting the best maintenance strategy. As a result of the application of the proposed integrated method in the manufacturing company, the ranking of the maintenance strategies was obtained, and predictive maintenance strategy was determined as the most appropriate maintenance strategy for the company.

References

  • Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42, 9565–9573.
  • Almedia, A.T., & Bohoris, G.A. (1995). Decision theory in maintenance decision making. Journal of Quality in Maintenance Engineering, 1(1), 39–45.
  • Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using fuzzy multiple criteria decision-making. International Journal of Production Economics, 84, 85–100.
  • Arabsheybani, A., Paydar, M. M., & Safaei A. S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk. Journal of Cleaner Production, 190, 577-591.
  • Archana, M., & Sujatha, V. (2012). Application of fuzzy MOORA and GRA in multi-criterion decision making problems. International Journal of Computer Applications, 53(9), 46-50.
  • Arjomandi, M. A., Dinmohammadi, F., Mosallanezhad, B., & Shafiee, M. (2021). A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets. Advances in Mechanical Engineering, 13(4), 1–21.
  • Arman, K. ve Kundakcı, N. (2022). Bulanık PIPRECIA yöntemi ile bankacılık endüstrisinde blokzincir teknolojisinin benimsenmesini etkileyen kritik faktörlerin değerlendirilmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25 (47), 79-92, doi: 10.31795/baunsobed.975891.
  • Azadivar, F., & Shu, V. (1999). Maintenance policy selection for JIT production systems. International Journal of Production Research, 37(16), 3725-3738, doi:10.1080/002075499190013.
  • Bakhat, R., & Rajaa, M. (2020). Development of a fuzzy hybrid approach for solving the maintenance strategy selection problem in macro systems. Strategy Management Logistics, ISSN:2509-0186.
  • Baležentis, A., Baležentis, T., & Brauers, W. K. M. (2012). Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Systems with Applications, 39(9), 7961–7967.
  • Bashiri, M., Badri, H., & Hejazi, T. H. (2011). Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Applied Mathematical Modelling, 35, 152–164.
  • Bera, A.K, Jana D. K., Banerjee, D., & Nandy, T. (2020). Supplier selection using extended IT2 fuzzy TOPSIS and IT2 fuzzy MOORA considering subjective and objective factors. Soft Computing, 24, 8899–8915.
  • Bertolini, M., & Bevilacqua, M. (2006). A combined goal programming—AHP approach to maintenance selection problem. Reliability Engineering and System Safety, 91(7), 839–848.
  • Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering and System Safety, 70, 71–83.
  • Blagojević, A., Stević, Ž., Marinković, D., Kasalica, S., & Rajilić, S. (2020). A novel Entropy-fuzzy PIPRECIA-DEA model for safety evaluation of railway traffic. Symmetry, 12, 1479, doi:10.3390/sym12091479.
  • Brauers, W. K. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35 (2), 445–469.
  • Carpitella, S., Mzougui, I., Benítez, J., Carpitella, F., Certa, A., Izquierdo, J., & Cascia, M. L. (2021). A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm. Reliability Engineering and System Safety, 205, 107265.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.
  • Cheng, Y.-H., & Tsao, H-L. (2010). Rolling stock maintenance strategy selection, sparesparts’ estimation, and replacements’ interval calculation. International Journal of Production Economics, 128, 404–412.
  • Đalić, I., Stević, Ž., Karamasa, C., & Puška, A. (2020a). A novel integrated fuzzy PIPRECIA–interval rough SAW model: green supplier selection. Decision Making. Applications in Management and Engineering, 3(1), 126-145.
  • Đalić, I., Ateljević, J., Stević, Ž., & Terzić, S. (2020b). An integrated Swot – fuzzy PIPRECIA model for analysis of competitiveness in order to improve logistics performances. Facta Universitatis Series: Mechanical Engineering, 18(3), 439-451.
  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. (2012). A MOORA based fuzzy multi-criteria decision-making approach for supply chain strategy selection. International Journal of Industrial Engineering Computations, 3, 649–662.
  • Dobrosavljević, A., Urošević, S., Vuković, M., Talijan, M., & Marinković, D. (2020). Evaluation of process orientation dimensions in the apparel industry. Sustainability, 12, 4145, doi:10.3390/su12104145.
  • Emovon, I., Norman, R.A., & Murphy, A.J. (2018). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29, 519–531.
  • Emovon, I., Okpako, O. S., & Edjokpa E. (2021). Application of fuzzy MOORA method in the design and fabrication of an automated hammering machine. World Journal of Engineering, 18(1), 37–49.
  • Ersöz, F., Kinci, C.H., & Ersöz, T. (2018). Model proposal for course selection with the fuzzy MOORA approach. European Journal of Science and Technology, 14, 369-377.
  • Gholami, J., Razavi, A., & Ghaffarpour, R. (2022). Decision-making regarding the best maintenance strategy for electrical equipment of buildings based on fuzzy analytic hierarchy process; case study: elevator. Journal of Quality in Maintenance Engineering, 28(3), 652-667.
  • Görener, A. (2013). Maintenance strategy selection by using WSA and TOPSIS methods under fuzzy decision environment. Journal of Engineering and Natural Sciences Sigma, 31, 159-177.
  • Ierace, S. & Cavalieri, S. (2008). Maintenance strategy selection: a comparison between fuzzy logic and analytic hierarchy process. 9th IFAC Workshop on Intelligent Manufacturing Systems. Szczecin, Poland, pp. 228-233.
  • Ighravwe, D. E., & Oke, S.A. (2020). A two‑stage fuzzy multi‑criteria approach for proactive maintenance strategy selection for manufacturing systems. N Applied Sciences, 2, 1683, doi:10.1007/s42452-020-03484-6.
  • Jafari, A., Jafarian, M. Zareei, A., & Zaerpour, F. (2008). Using fuzzy Delphi method in maintenance strategy selection problem. Journal of Uncertain Systems, 2(4), 289–298.
  • Jiménez, J. J. M., Vingerhoeds, R., Grabot, B., & Schwartz, S. (2021). An ontology model for maintenance strategy selection and assessment. Journal of Intelligent Manufacturing, doi: 10.1007/s10845-021-01855-3.
  • Jocic, K. J., Jocic, G., Karabasevic, D., Popovic, G., Stanujkic, D., Zavadskas E. K., & Nguyen, P.T. (2020). A novel integrated PIPRECIA–interval-valued triangular fuzzy ARAS model: e-learning course selection. Symmetry, 12, 928, doi:10.3390/sym12060928.
  • Karande, P., & Chakraborty, S. (2012). A fuzzy-MOORA approach for ERP system selection. Decision Science Letters, 1, 11–22.
  • Khorshidi, M., Erkayman, B., Albayrak, Ö. Kılıç, R., & Demir, H.İ. (2022). Solar power plant location selection using integrated fuzzy DEMATEL and fuzzy MOORA method. International Journal of Ambient Energy, doi: 10.1080/01430750.2022.2068067.
  • Lopez, J. C., & Kolios, A. (2022). Risk-based maintenance strategy selection for wind turbine composite Blades. Energy Reports, 8, 5541–5561.
  • Mandal, U. K., & Sarkar, B. (2012). Selection of best intelligent manufacturing system (IMS) under fuzzy MOORA conflicting MCDM environment. International Journal of Emerging Technology and Advanced Engineering, 2(9), 301-310.
  • Matawale, C. R., Datta, S., Mahapatra, S.S. (2016). Supplier selection in agile supply chain: Application potential of FMLMCDM approach in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA. Benchmarking: An International Journal, 23(7), 2027-2060.
  • Mavi, R. K., Goh, M., & Zarbakhshnia, N. (2017). Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. The International Journal of Advanced Manufacturing Technology, 91, 2401–2418.
  • Mechefske, C.K., & Wang, Z. (2001). Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies. Mechanical Systems and Signal Processing, 17(2), 305–316.
  • Memiş, S., Demir, E., Karamaşa, Ç., & Korucuk, S. (2020). Prioritization of road transportation risks: an application in Giresun province, Operational Research in Engineering Sciences: Theory and Applications, 3(2), 111-126.
  • Momeni, M., Fathi, M. R., Zarchi, M. K., & Azizollahi, S. (2011). A fuzzy TOPSIS-based approach to maintenance strategy selection: a case study. Middle-East Journal of Scientific Research, 8(3), 699-706.
  • Özdağoğlu, A. Keleş, M. K., & Işıldak, B. (2021). Evaluation of the world's busiest airports with PIPRECIA-E, SMART and MARCOS methods. Erciyes University Journal of Faculty of Economics and Business Administrative Sciences, 58, 333-352.
  • Pariazar, M., Shahrabi, J., Zaeri, M.S., & Parhizi, S. (2008). A combined approach for maintenance strategy selection. Journal of Applied Sciences, 8(23), 4321-4329.
  • Patil, A., Soni, G., Prakash, A., & Karwasra, K. (2022). Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches. International Journal of Quality & Reliability Management, 39(3), 675-703.
  • Pérez-Domínguez, L., Alvarado-Iniesta, A., Rodríguez-Borbón, I. & Vergara-Villegas, O. (2015). Intuitionistic fuzzy MOORA for supplier selection. DYNA, 82(191), 34-41.
  • Shafiee, M. (2015). Maintenance strategy selection problem: an MCDM overview. Journal of Quality in Maintenance Engineering, 21(4), 378-402.
  • Stanujkic, D., Kazimieras Zavadskas, E., Karabasevic, D., Smarandache, F. & Turskis, Z., (2017). The use of the pivot pairwise relative criteria importance assessment method for determining the weights of criteria, Romanian Journal of Economics, 20(4), 116-133.
  • Stevenson, W. J. (2007). Operations Management, New York: McGraw-Hill/Irwin.
  • Stević, Ž., Stjepanović, Ž., Božićković, Z. Das, D. K., & Stanujkić, D. (2018). Assessment of conditions for implementing information technology in a warehouse system: a novel fuzzy PIPRECIA method. Symmetry, 10, 586, doi:10.3390/sym10110586.
  • Tomašević, M., Lapuh, L., Stević, Ž., Stanujkić, D., & Karabašević, D. (2020). Evaluation of criteria for the implementation of High-Performance Computing (HPC) in Danube region countries using fuzzy PIPRECIA method. Sustainability, 12, 3017, doi:10.3390/su12073017.
  • Triantaphyllou, E. Kovalerchuk, B. Mann, L., & Knapp, G., (1997). Determining the most important criteria in maintenance decision making. Journal of Quality in Maintenance Engineering, 3(1), 16–28.
  • Tuyet, N.T.A., & Chou, S.-Y. (2018). Maintenance strategy selection for improving cost-effectiveness of offshore wind systems. Energy Conversion and Management, 157, 86–95.
  • Vesković, S., Milinković, S., Abramović, B., & Ljubaj, I. (2020a). Determining criteria significance in selecting reach stackers by applying the fuzzy PIPRECIA method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 72-88.
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There are 60 citations in total.

Details

Primary Language English
Subjects Operation
Journal Section Research Articles
Authors

Nilsen Kundakcı 0000-0002-7283-320X

Publication Date June 30, 2023
Published in Issue Year 2023 Volume: 10 Issue: 2

Cite

APA Kundakcı, N. (2023). Integration of Fuzzy PIPRECIA and Fuzzy MOORA Methods for Maintenance Strategy Selection. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 10(2), 401-423. https://doi.org/10.47097/piar.1256081

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