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

  • Research Article - Systems Engineering
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Abstract

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.

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References

  1. Ayres, R.: (1) Future trends in factory automation, (2) Technology forecasts for CIM. Manuf. Rev. 1, 2 (1989)

    Google Scholar 

  2. Jain, A.S.; Meeran, S.: Deterministic job-shop scheduling: past, present and future. Eur. J. Oper. Res. 113, 390–434 (1999)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  5. Groover, T.A.: Impedance Cardiography: Techniques Applicable to Extrapolation. University of Texas at Austin, Austin (1981)

    Google Scholar 

  6. Jeang, A.: Reliable tool replacement policy for quality and cost. Eur. J. Oper. Res. 108, 334–344 (1998)

    Article  MATH  Google Scholar 

  7. Jeang, A.: Tool replacement policy for probabilistic tool life and random wear process. Qual. Reliab. Eng. Int. 15, 205–212 (1999)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. Vagnorius, Z.; Rausand, M.; Sørby, K.: Determining optimal replacement time for metal cutting tools. Eur. J. Oper. Res. 206, 407–416 (2010)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  15. Salonitis, K.; Kolios, A.: Reliability assessment of cutting tools life based on advanced approximation methods. Procedia CIRP 8, 397–402 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Asadzadeh, S.; Aghaie, A.: Improving the product reliability in multistage manufacturing and service operations. Qual. Reliab. Eng. Int. 28, 397–407 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Garg, H.; Sharma, S.P.: Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Comput. Ind. Eng. 64, 247–255 (2013)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  27. Garg, H.: An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol. Comput. 24, 1–10 (2015)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  30. Garg, H.: Reliability, availability and maintainability analysis of industrial systems using PSO and fuzzy methodology. Mapan 29, 115–129 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  35. Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)

    MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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). https://doi.org/10.1007/s40314-016-0402-0

    Google Scholar 

  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. Chern, M.-S.: On the computational complexity of reliability redundancy allocation in a series system. Oper. Res. Lett. 11, 309–315 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  41. Goldberg, D.E.; Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95–99 (1988)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Taguchi, G.: Introduction to Quality Engineering. Asian Productivity Organization, UNIPUB, White Plains, New York (1986)

    Google Scholar 

  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)

    Article  Google Scholar 

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Correspondence to S. T. A. Niaki.

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Karimi, B., Niaki, S.T.A., Haleh, H. et al. Reliability optimization of tools with increasing failure rates in a flexible manufacturing system. Arab J Sci Eng 44, 2579–2596 (2019). https://doi.org/10.1007/s13369-018-3309-9

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  • DOI: https://doi.org/10.1007/s13369-018-3309-9

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