Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 2579–2596 | Cite as

Reliability optimization of tools with increasing failure rates in a flexible manufacturing system

  • Behzad Karimi
  • S. T. A. NiakiEmail author
  • Hassan Haleh
  • Bahman Naderi
Research Article - Systems Engineering


Tool reliability is one of the most important issues in a flexible manufacturing system. If a tool fails to operate correctly, the performance of the manufacturing system is reduced, the due date may be violated, or the product quality falls behind the standards. This paper develops a bi-objective mathematical model for tool selection in a flexible manufacturing system in order to optimize both reliability and cost. The tools in these environments are considered to have increasing failure rates as they are used over time; a case closer to reality. This paper aims to evaluate the availability of different tools used in a production system, in which the reliability of a tool is dependent on the failure occurring to any other compatible tool. Two multi-objective genetic algorithms along with the \(\varepsilon \)-constraint method are proposed to solve the problem. The Taguchi method is also employed to calibrate the parameters of the proposed algorithms and to enhance their performances. Finally, a hybrid AHP-TOPSIS is utilized to prioritize the solution algorithms. The results indicate that while the \(\varepsilon \)-constraint is the best to solve small-size problems, the non-dominated rank genetic algorithm performs the best in solving large-size problems.


Tool reliability Flexible manufacturing systems Increasing failure rate Multi-objective optimization AHP-TOPSIS 


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  1. 1.
    Ayres, R.: (1) Future trends in factory automation, (2) Technology forecasts for CIM. Manuf. Rev. 1, 2 (1989)Google Scholar
  2. 2.
    Jain, A.S.; Meeran, S.: Deterministic job-shop scheduling: past, present and future. Eur. J. Oper. Res. 113, 390–434 (1999)CrossRefzbMATHGoogle Scholar
  3. 3.
    Palei, S.: Optimization of choosing the rule for changing a cutting tool in exploitation of the flexible manufacturing module. Stanki Instrum. 11, 27–30 (1988)Google Scholar
  4. 4.
    Al-Fawzan, M.; Al-Sultan, K.: A tabu search based algorithm for minimizing the number of tool switches on a flexible machine. Comput. Ind. Eng. 44, 35–47 (2003)CrossRefGoogle Scholar
  5. 5.
    Groover, T.A.: Impedance Cardiography: Techniques Applicable to Extrapolation. University of Texas at Austin, Austin (1981)Google Scholar
  6. 6.
    Jeang, A.: Reliable tool replacement policy for quality and cost. Eur. J. Oper. Res. 108, 334–344 (1998)CrossRefzbMATHGoogle Scholar
  7. 7.
    Jeang, A.: Tool replacement policy for probabilistic tool life and random wear process. Qual. Reliab. Eng. Int. 15, 205–212 (1999)CrossRefGoogle Scholar
  8. 8.
    Buyurgan, N.; Saygin, C.; Kilic, S.E.: Tool allocation in flexible manufacturing systems with tool alternatives. Robot. Comput. Integr. Manuf. 20, 341–349 (2004)CrossRefGoogle Scholar
  9. 9.
    Sun, J.-W.; Xi, L.-F.; Du, S.-C.; Ju, B.: Reliability modeling and analysis of serial-parallel hybrid multi-operational manufacturing system considering dimensional quality, tool degradation and system configuration. Int. J. Prod. Econ. 114, 149–164 (2008)CrossRefGoogle Scholar
  10. 10.
    Mahdavi, I.; Jazayeri, A.; Jahromi, M.; Jafari, R.; Iranmanesh, H.: P-ACO approach to assignment problem in FMSs. World Acad. Sci. Eng. Technol. 42, 196–203 (2008)Google Scholar
  11. 11.
    Hsu, B.-M.; Shu, M.-H.: Reliability assessment and replacement for machine tools under wear deterioration. Int. J. Adv. Manuf. Technol. 48, 355–365 (2010)CrossRefGoogle Scholar
  12. 12.
    Rodriguez, C.E.P.; De Souza, G.F.M.: Reliability concepts applied to cutting tool change time. Reliab. Eng. Syst. Saf. 95, 866–873 (2010)CrossRefGoogle Scholar
  13. 13.
    Vagnorius, Z.; Rausand, M.; Sørby, K.: Determining optimal replacement time for metal cutting tools. Eur. J. Oper. Res. 206, 407–416 (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chen, B.; Chen, X.; Li, B.; He, Z.; Cao, H.; Cai, G.: Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mech. Syst. Signal Process. 25, 2526–2537 (2011)CrossRefGoogle Scholar
  15. 15.
    Salonitis, K.; Kolios, A.: Reliability assessment of cutting tools life based on advanced approximation methods. Procedia CIRP 8, 397–402 (2013)CrossRefGoogle Scholar
  16. 16.
    Salonitis, K.; Kolios, A.: Reliability assessment of cutting tool life based on surrogate approximation methods. Int. J. Adv. Manuf. Technol. 71, 1197–1208 (2014)CrossRefGoogle Scholar
  17. 17.
    Sgarbossa, F.; Persona, A.; Pham, H.: Using systemability function for periodic replacement policy in real environments. Qual. Reliab. Eng. Int. 31, 617–633 (2015)CrossRefGoogle Scholar
  18. 18.
    Liu, S.; Hu, Y.; Liu, C.; Zhang, H.: Real-time reliability self-assessment in milling tools operation. Qual. Reliab. Eng. Int. 32, 2245–2252 (2016)CrossRefGoogle Scholar
  19. 19.
    Lugtigheid, D.; Jardine, A.K.; Jiang, X.: Optimizing the performance of a repairable system under a maintenance and repair contract. Qual. Reliab. Eng. Int. 23, 943–960 (2007)CrossRefGoogle Scholar
  20. 20.
    Asadzadeh, S.; Aghaie, A.: Improving the product reliability in multistage manufacturing and service operations. Qual. Reliab. Eng. Int. 28, 397–407 (2012)CrossRefGoogle Scholar
  21. 21.
    Bennane, A.; Yacout, S.: LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance. J. Intell. Manuf. 23, 265–275 (2012)CrossRefGoogle Scholar
  22. 22.
    Wu, Y.; Hong, G.; Wong, W.: Prognosis of the probability of failure in tool condition monitoring application—a time series based approach. Int. J. Adv. Manuf. Technol. 76, 513–521 (2015)CrossRefGoogle Scholar
  23. 23.
    Aghdam, B.; Vahdati, M.; Sadeghi, M.: Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int. J. Adv. Manuf. Technol. 76, 1631–1642 (2015)CrossRefGoogle Scholar
  24. 24.
    Letot, C.; Serra, R.; Dossevi, M.; Dehombreux, P.: Cutting tools reliability and residual life prediction from degradation indicators in turning process. Int. J. Adv. Manuf. Technol. 86, 495–506 (2016)CrossRefGoogle Scholar
  25. 25.
    Garg, H.; Sharma, S.P.: Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Comput. Ind. Eng. 64, 247–255 (2013)CrossRefGoogle Scholar
  26. 26.
    Soltani, R.; Sadjadi, S.J.; Tofigh, A.A.: A model to enhance the reliability of the serial parallel systems with component mixing. Appl. Math. Model. 38, 1064–1076 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Garg, H.: An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol. Comput. 24, 1–10 (2015)CrossRefGoogle Scholar
  28. 28.
    Miriha, M.; Niaki, S.T.A.; Karimi, B.; Zaretalab, A.: Bi-objective reliability optimization of switch-mode k-out-of-n series-parallel systems with active and cold standby components having failure rates dependent on the number of components. Arab. J. Sci. Eng. 42, 5305–5320 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Garg, H.; Rani, M.; Sharma, S.P.; Vishwakarma, Y.: Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system. J. Manuf. Syst. 33, 335–347 (2014)CrossRefGoogle Scholar
  30. 30.
    Garg, H.: Reliability, availability and maintainability analysis of industrial systems using PSO and fuzzy methodology. Mapan 29, 115–129 (2014)CrossRefGoogle Scholar
  31. 31.
    Garg, H.; Rani, M.; Sharma, S.P.; Vishwakarma, Y.: Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment. Expert Syst. Appl. 41, 3157–3167 (2014)CrossRefGoogle Scholar
  32. 32.
    Garg, H.: An approach for analyzing the reliability of industrial system using fuzzy Kolmogorov’s differential equations. Arab. J. Sci. Eng. 40, 975–987 (2015)CrossRefzbMATHGoogle Scholar
  33. 33.
    Garg, H.: An approach for solving constrained reliability-redundancy allocation problems using Cuckoo search algorithm. Beni-Suef Univ. J. Basic Appl. Sci. 4, 14–25 (2015)CrossRefGoogle Scholar
  34. 34.
    Rani, D.; Gulati, T.R.; Garg, H.: Multi-objective non-linear programming problem in intuitionistic fuzzy environment: optimistic and pessimistic view point. Expert Syst. Appl. 64, 228–238 (2016)CrossRefGoogle Scholar
  35. 35.
    Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)MathSciNetGoogle Scholar
  36. 36.
    Garg, H.: Performance analysis of an industrial system using soft computing based hybridized technique. J Braz. Soc. Mech. Sci. Eng. 39, 1441–1451 (2017)CrossRefGoogle Scholar
  37. 37.
    Kumar, K.; Garg, H.: Connection number of set pair analysis based TOPSIS method on intuitionistic fuzzy sets and their application to decision making. Appl. Intell. 1, 1–8 (2017)Google Scholar
  38. 38.
    Kumar, K.; Garg, H.: TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment. Comput. Appl. Math. 1, 1–11 (2016). Google Scholar
  39. 39.
    Sharifi, M.; Memariani, A.; Noorossana, R.: Real time study of a k-out-of-n system: n identical elements with increasing failure rates. Iran. J. Oper. Res. 1, 56–67 (2009)Google Scholar
  40. 40.
    Chern, M.-S.: On the computational complexity of reliability redundancy allocation in a series system. Oper. Res. Lett. 11, 309–315 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Goldberg, D.E.; Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95–99 (1988)CrossRefGoogle Scholar
  42. 42.
    Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRefGoogle Scholar
  43. 43.
    Jadaan, O.A.; Rajamani, L.; Rao, C.R.: Non-dominated ranked genetic algorithm for solving constrained multi-objective optimization problems. J. Theor. Appl. Inf. Technol. 5, 714–725 (2009)Google Scholar
  44. 44.
    Taguchi, G.: Introduction to Quality Engineering. Asian Productivity Organization, UNIPUB, White Plains, New York (1986)Google Scholar
  45. 45.
    Rahmati, S.H.A.; Hajipour, V.; Niaki, S.T.A.: A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl. Soft Comput. 13, 1728–1740 (2013)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Industrial Engineering, Faculty of Industrial and Mechanical EngineeringIslamic Azad UniversityQazvinIran
  2. 2.Department of Industrial EngineeringSharif University of TechnologyTehranIran
  3. 3.Faculty of Industrial and Mechanical EngineeringIslamic Azad UniversityQazvinIran
  4. 4.Department of Industrial EngineeringKharazmi UniversityTehranIran

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