نوع مقاله : علمی _ پژوهشی

نویسنده

دانشجوی دکتری مدیریت تولید و عملیات، دانشگاه علامه طباطبائی

چکیده

هدف از این پژوهش ارزیابی استراتژی­های مختلف نگهداری و تعمیرات برای ماشین­آلات سازمان می­باشد. تلاش شده است تا مناسب­ترین استراتژی نگهداری و تعمیرات برای ماشین­آلات و تجهیزات به­گونه­ای انتخاب شود که سطح قابلیت اطمینان تجهیزات و ماشین­آلات را بدون افزایش در سرمایه‌گذاری، افزایش‌‌دهد. از آنجایی که انتخاب مناسب­ترین استراتژی نگهداری و تعمیرات یک مساله تصمیم‌گیری چند معیاره است، بنابراین تصمیم‌گیرندگان در ارزیابی گزینه­ها و معیارهای پژوهش از ترجیحاتی استفاده می‌کنند که غیرقطعی است. از این رو در این پژوهش از مفهوم تئوری مجموعه راف که در چنین شرایطی کارآمد می­باشد استفاده می­شود. در واقع در این پژوهش ابتدا با استفاده از تحلیل عاملی تاییدی به ارزیابی معیارها و عوامل پژوهش پرداخته می­شود و سپس از مفهوم تئوری راف برای تبدیل ترجیحات خبرگان به اعداد فاصله‌ای و از روش­های فرایند تحلیل سلسله مراتبی راف و تاپسیس راف برای ارزیابی و رتبه‌بندی گزینه­ها استفاده می‌شود. قابل ذکر است که در این مقاله، روش تاپسیس با داده­های راف توسعه داده شده است. در پایان نیز به منظور نشان­دادن  قابلیت کاربردی بودن روش مطرح شده، آن را در یک مورد مطالعاتی استفاده نموده­ایم. نتایج حاصل از این پژوهش نشان می­دهد که بکارگیری مفهوم تئوری راف به همراه روش­های تصمیم­گیری چند شاخصه می­تواند به مدیران سازمان­ها در امر تصمیم­گیری در شرایط عدم وجود اطلاعات دقیق کمک نماید.

کلیدواژه‌ها

عنوان مقاله [English]

Applying Concept of Rough Set Theory with Multi-Criteria Decision-Making Methods to Evaluate and Select the Most Appropriate Maintenance Strategy

نویسنده [English]

  • Mohammad Sabet Motlagh

....

چکیده [English]

 
The main purpose of this study is to evaluate different maintenance strategies for machinery and equipment. We attempt to select the most appropriate maintenance strategy for increasing availability and reliability levels of production facilities without a great increasing of investment. Since the nature of maintenance strategy selection is a multi-criteria problem, so decision makers to evaluation of criteria and alternatives using of uncertain preferences. Therefore, in this study we will use the concept of Rough set theory that are efficient in such circumstances. In fact, in this study first we will use confirmatory factor analysis to evaluating of research criteria and factors and then we will use the concept of Rough theory to convert preferences of decision makers to interval numbers and Multi-criteria decision-making methods (Rough AHP & Rough Topsis) for evaluating and ranking the maintenance strategies. It is noteworthy that in this paper, TOPSIS method with Rough data has been developed. finally, in order to demonstrate the applicability of the proposed method, we have used it in a case. The results show that using such model can help decision makers to make better decisions in the absence of full information.

کلیدواژه‌ها [English]

  • Maintenance Strategies
  • Rough Set Theory
  • Rough TOPSIS- Rough Analytical Hierarchy Process
Arunraj, N & Maiti, J (2010). Risk-based maintenance policy selection using AHP and goal programming .Sofety science, 48(2), 238-247.
Azadivar, F & Shu, V (1999). Maintenance policy selection for JIT production systems.International jcurnal of production research, 37(16), 3725-3738.
Bali, O, Kose, E & Gumus, S (2013). Green supplier selection based on IFS and GRA.Grey systems: theory and Application, 3(2), 158-176.
Bengtsson, M (2004). Condition Based Maintenance System Technology – Where is Development Heading.condition based maintenance systems-An investigation of Technical Constituents and organizational Aspects,55.
Bertolini, M & Bevilacqua, M (2006). A combined goal programming—AHP approach to maintenance selection problem. Reliability Engineering & Systems Safety, 91(7), 839-848.
Bruno, G, Esposito, E, Genovese, A & Simpson, M (2015). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment.Expert systems with Application,43,271-285.
Burns , P (1997). Advanced integrated maintenance strategie. Issues of A FE Facilities Management Journal, 27 – 36
Cao, J, Cao, G & Wang,­W (2012). A hybrid model using analytic network process and gray relational analysis for bank's IT outsourcing vendor selection.kybernets,47(7/8) , 994-1013.
Chen, L. F & Tsai, C.-T (2016). Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain. Tourism Management, 53, 197-206.
Fakoor Saghih, A. M (2016). Measuring the Flexibility of Supply Chain by Using Gray System. Management Research in Iran, 19(4), 117-138.
Hon Yin Lee, H & Scott, D (2009). Strategic and operational factors' influence on the management of building maintenance operation processes in sports and leisure facilities, Hong Kong. Journal of Retail & Leisure Property, 8(1), 25-37. doi:10.1057/rlp.2008.29.
Hylocka, R & Currimb, F (2013). A maintenance centric approach to the view selection problem. A maintenance centric approach to the view selection problem, 38(7), 971–987.
Ilker, M. A, Coşkun, H & Birdogan, B (2013). Business School ranking with grey relational analysis: the case of Turkey. Grey Systems: Theory and Application, 3(1), 76-94. doi:doi:10.1108/20439371311293714
Ishizaka, A & Nemery, P (2014). Assigning machines to incomparable maintenance strategies with ELECTRE-SORT. Omega, 47, 45-59.
Karsak, E. E & Dursun, M (2015). An integrated fuzzy MCDM approach for supplier evaluation and selection. Computers & Industrial Engineering, 82, 82-93.
Kose, E, Kabak, M & Aplak, H (2013). Grey theory based MCDM procedure for sniper selection problem.Grey systems: theory and application, 3(1), 35-45.
Luce, S (1999). Choice criteria in conditional preventive maintenance. Mechanical Systems and Signal Processing, 13(1), 163-168.
Oberg, C. P (2002). Managing maintenance as a business.EPAC Software Technologies.
Orji, I. J & Wei, S (2015). An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry. Computers & Industrial Engineering, 88, 1-12.
Pawlak, Z (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356.
Ramesh, S, Viswanathan, R & Ambika, S (2016). Measurement and optimization of surface roughness and tool wear via grey relational analysis, TOPSIS and RSA techniques. Measurement, 78, 63-72.
Sabet Motlagh, M, Salehi Sadaghiani, j, Ayazi, S. A & Abedini Naeini, M (2015). Evaluation and Selection of Strategic Suppliers Using Integrated approach of AHP and Gray  TOPSIS. Journal of Operational Research and Its Applications, 11(4), 101-117 (in persian).
Sanjeev, G & Sandeep, G (2012). Applying fuzzy grey relational analysis for ranking the advanced manufacturing systems. Grey Systems: Theory and Application, 2(2), 284-298.
Shafiee Nick Abadi, M, Farajpour Khanaposhtani, H, Eftekhari, H & Sadadadi, A (2016). Using hybrid approach FA, AHP and TOPSIS for selecting and ranking the appropriate maintenance strategies. Industrial Management Studies, 13(39), 35-62(in persian).
Shen, G (1997). A comparative study of priority setting methods for planned maintenance of public buildings.Facilities,15(12/13) , 331-339.
Stadnicka, D, Antosz, K & Ratnayake, R. M (2014). Development of an empirical formula for machine classification: Prioritization of maintenance tasks. safety science, 63,34-41.
Tsang, A (1998). A Strategic approach to managing maintenance performance. J of Qual in Maint Eng 4(2): 87-94.Journal of Quality in maintenance Engineeriry,4(2),87-94.
Van Horenbeek, A & Pintelon, L (2014). Development of a maintenance performance measurement framework-using the analytic network process (ANP) for maintenance performance indicator selection. Omega, 42(1), 33-46.
Zhai, L. Y, Khoo, L.P & Zhong, Z. W (2008). A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology, 37(5), 613-624.
Zhu, G, Hu, J, Qi, J, Gu, C.C & Peng, Y (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number.Advanced Engineeriny Informatics, 29(3),408-418.