Evaluation of optimum maintenance and repair strategy by multi-criteria decision-making method in textile industry

Ehsan LESANI, Habibollah JAVANMARD
1.348 464


Abstract. One of the most important problems in production management and operations is maintenance & repair. Setting up a proper maintenance & repair program prevents either unexpected failures and production disorders or time loss and expensive costs. On the other hand, this program increases useful life of machinery and keeps moderate level of productivity. The goal of this research is identification of suitable criterion for selection of an optimum maintenance & repair strategy, with their weighted importance, by Fuzzy-ANP method in textile industry. Thus, a model was designed for selection of proper criteria for maintenance & repair. After identification of proper criteria, the optimum maintenance & repair strategy in textile industry was selected by Expert Analysis method by a questionnaire. Then ANP technique was used to determine weights of indices. In this step, views of experts for pair comparisons of indices and their weights were extracted from the questionnaires. Finally, the results show that preventive repair strategy has the highest score of 0.43703, situation-based strategy with 0.242812, and predictive strategy with 0.16236. The score of maintenance & repair based on reliability is 0.157798, which is the lowest score.


Maintenance & repair, multi-criteria decision-making, Fuzzy-ANP method, textile industry

Full Text:



Asgharpur, M. (2009), “Multi-criteria decisions”, University of Tehran, 7th edition.

Bujadeziov; Mohammad Hosseini, J. (2002), “Fuzzy logics and its application in management”, Tehran, Ishif Publications, 1st edition.

Khaki, Gh. (2008), “Research method by an approach to thesis writing”, Tehran, Baztab Publications, 4th edition.

Delavar, A. (2006), “Research methods in behavioral sciences”, Tehran, Agah Publications.

Kolahan, F.; Dustparast, Mohammad; Mamurian, Mojtaba (2007), “Determination of optimum preventive maintenance and repair type and schedule in multi-component systems by reliability”, Journal of Technical Faculty, pp. 511-523.

Albert H.C. Tsang; W.K. Yeung; Andrew K.S.; Jardine, Bartholomew; P.K. Leung (2006), “Data management for CBM optimization,Journal of Quality in Maintenance Engineering”, Vol. 12, No. 1, pp. 37-51.

Banjevic, D.; Jardine, A.K.S.; Makis, V.; Ennis, E. (2001،٬ ), “A control-limit policy and software for CBM”, INFOR, Vol. 39, No. 1, pp. 32-50.

Bansal, D.; Evansb DJ.; Jones B. (2005), “Application of a real-time predictive maintenance system to a production machine system”, Journal of Machine tools and manufacture; 1210-1221.

Franc, Ois; Boulet, Jean; Ali Gharbi (2009), ”Multiobjective optimization in an unreliable failure-prone manufacturing system”, Journal of Quality in Maintenance Engineering, Vol. 15, No. 4, pp. 397-411.

Hax, A.C.; Majluf, N.S. (1991), “The Strategy Concept and Process: A Pragmatic Approach”, Prentice-Hall International Inc., New Jersey.

Lie, C.H.; Chun, Y.H. (1986), “An algorithm for preventive maintenance policy”, IEEE Trans Reliab, Vol. 35, No. 1, PP.71-5.

Lin R.H. (2008), “An integrated FANP–MOLP for supplier evaluation and order allocation”, Applied Mathematical Modeling, Article in Press.

Mann, L. Jr.; Anuj, S.; Gerald, M.K. (1995), “Statistical-based or condition-based preventive maintenance?”, Journal of Quality in Maintenance Engineering, Vol. 1, No. 1, pp. 46-59.

Saaty, Thomas L. (1999), “Fundamentals of the Analytic Network Process”, ISAHP Japan, pp. 12-14.

Saaty, Thomas L. (1990) “The Analytic Hierarchy Process”, RWS Publications, Pittsburg, pp. 184-192.

Shyjith, K.; Ilangkumaran, Kumanan M.S. (2008), “Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry”, Journal of Quality in Maintenance Engineering, Vol. 14, No. 4, pp. 375-386.

Sipahi, Seyhan; Mehpare, Timor (2010), ”The analytic hierarchy process and analytic network process: An overview of applications”, Management Decision, Vol. 48, No. 5, pp. 775-808.