Skip to main content

Advertisement

SpringerLink
Log in

Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This study establishes a bi-objective imperfect preventive maintenance (BOIPM) model of a series-parallel system. The improvement factor method is used to evaluate the extent to which repairing components can restore the system reliability. The total maintenance cost and mean system reliability are optimized simultaneously through determining the most appropriate maintenance alternative. A bi-objective hybrid genetic algorithm (BOHGA) is established to optimize the BOIPM model. The BOHGA utilizes a Pareto-based technique to determine and retain the superior chromosomes as the GA chromosome evolutions are performed. Additionally, a unit-cost cumulative reliability expectation measure (UCCREM) is developed to evaluate the extent to which maintaining each individual component benefits the total maintenance cost and system reliability over the operational lifetime. This UCCREM is then incorporated into the genetic algorithm to construct a superior initial chromosome population and thereby enhance its solution efficiency. In order to obtain diverse bi-objective solutions as the Pareto-efficient frontier is approached, the closeness metric and diversity metric are employed to evaluate the superiority of the non-dominated solutions. Accordingly, decision makers can easily determine the most appropriate maintenance alternative. Three simulated cases verify the efficacy and practicality of this approach for determining an imperfect preventive maintenance strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Bai, J.,& Pham, H. (2006). Cost analysis on renewable full-service warranties for multi-component systems. European Journal of Operational Research, 168, 492–508.

    Google Scholar 

  • Baker, B. M.,& Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers and Operations Research, 30, 787–800.

    Article  Google Scholar 

  • Berrichi, A., Amodeo, L., Yalaoui, F., Châtelet, E.,& Mezghiche, M. (2009). Bi-objective optimization algorithms for joint production and maintenance scheduling: Application to the parallel machine problem. Journal of Intelligent Manufacturing, 20, 389–400.

    Google Scholar 

  • Bris, R., Chatelet, E.,& Yalaoui, F. (2003). New method to minimize the preventive maintenance cost of series-parallel systems. Reliability Engineering& System Safety, 82, 247–255.

    Google Scholar 

  • Busacca, P., Marseguerra, M.,& Zio, E. (2001). Multi-objective optimization by genetic algorithms: Application to safety systems. Reliability Engineering& System Safety, 72, 59–74.

    Google Scholar 

  • Castro, I. T. (2009). A model of imperfect preventive maintenance with dependent failure modes. European Journal of Operational Research, 196, 217–224.

    Article  Google Scholar 

  • Coello, C. A. C. (1999). A comprehensive survey of evolutionary-based multi-objective optimization techniques. International Journal of Knowledge and Information System, 1(3), 269–308.

    Article  Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A.,& Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Google Scholar 

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. MA: Wiley.

    Google Scholar 

  • Elsayed, A. (1996). Reliability engineering. MA: Wiley.

    Google Scholar 

  • Fonseca, C. M.,& Fleming, P. J. (1998). Multi-objective optimization and multiple constraint handling with evolutionary algorithms—part I: A unified formulation. IEEE Transactions on SMC—Part A: System and Humans, 28(1), 26–37.

    Google Scholar 

  • Fonseca, C. M.,& Fleming, P. J. (1997). Multi-objective optimization. NY: Oxford (Handbook of Evolutionary Computation).

    Google Scholar 

  • Gen, M.,& Cheng, R. (1997). Genetic algorithms and engineering design (pp. 133–172). MA: Wiley.

  • Harrington, E. (1965). The desirability function. Industrial Quality Control, 21(10), 494–498.

    Google Scholar 

  • Hsieh, Y. C., Chen, T. C.,& Bricker, D. L. (1998). Genetic algorithms for reliability design problems. Microelectronics Reliability, 38, 1599–1605.

    Google Scholar 

  • Khatab, A., Rezg, N.,& Ait-Kadi, D. (2011). Optimum block replacement policy over a random time horizon. Journal of Intelligent Manufacturing, 22(6), 885–889.

    Google Scholar 

  • Konak, A., Coit, D. W.,& Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering& System Safety, 91, 992–1007.

    Google Scholar 

  • Levitin, G.,& Lisnianski, A. (2000). Optimization of imperfect preventive maintenance for multi-state systems. Reliability Engineering& System Safety, 67, 193–203.

    Article  Google Scholar 

  • Liao, W., Pan, E.,& Xi, L. (2010). Preventive maintenance scheduling for repairable system with deterioration. Journal of Intelligent Manufacturing, 21(6), 875–884.

    Google Scholar 

  • Lin, T. W.,& Wang, C. H. (2012). A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system. Journal of Intelligent Manufacturing, 23(4), 1225–1236 .

    Google Scholar 

  • Montgomery, D. C. (2005). Design and analysis of experiments (6th ed.). NY: Wiley.

    Google Scholar 

  • Moradi, E., Fatemi Ghomi, S. M. T.,& Zandieh, M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Systems with Applications, 38(6), 7169–7178.

  • Nahas, N., Khatab, A., Ait-Kadi, D.,& Nourelfath, M. (2008). Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems. Reliability Engineering& System Safety, 93, 1658–1672.

    Google Scholar 

  • Nakagawa, T. (2005). Maintenance theory of reliability. London: Springer.

    Google Scholar 

  • Nosoohi, I.,& Hejazi, S. R. (2011). A multi-objective approach to simultaneous determination of spare part numbers and preventive replacement times. Applied Mathematical Modelling, 35, 1157–1166.

    Article  Google Scholar 

  • Osyczka, A.,& Kundu, S. (1996). A modified distance method for multi-criteria optimization, using genetic algorithms. Computers& Industrial Engineering, 30, 871–882.

    Article  Google Scholar 

  • Park, D. H., Jung, G. M.,& Yum, J. K. (2000). Cost minimization for periodic maintenance policy of a system subject to slow degradation. Reliability Engineering& System Safety, 68, 105–112.

    Google Scholar 

  • Pham, H.,& Wang, H. Z. (1996). Imperfect maintenance. European Journal of Operational Research, 94, 425–438.

    Article  Google Scholar 

  • Quan, G., Greenwood, G. W., Donglin, L.,& Sharon, H. (2007). Searching for multi-objective preventive maintenance schedules: Combining preferences with evolutionary algorithms. European Journal of Operational Research, 177, 1969–1984.

    Google Scholar 

  • Rudolph, G. (2001). Evolutionary search under partially ordered fitness sets. In Proceedings of the international symposium on information science innovations in engineering of natural and artificial intelligent systems, pp. 818–822.

  • Sachdeva, A., Kumar, D.,& Kumar, P. (2008). Planning and optimizing the maintenance of paper production systems in a paper plant. Computers and Industrial Engineering, 55, 817–829.

    Google Scholar 

  • Schutz, J., Rezg, N.,& Léger, J.-B. (2011). An integrated strategy for efficient business plan and maintenance plan for systems with a dynamic failure distribution. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0543-3.

  • Soro, I. W., Nourelfath, M.,& Aıt-Kadi, D. (2010). Performance evaluation of multi-state degraded systems with minimal repairs and imperfect preventive maintenance. Reliability Engineering& System Safety, 95, 65–69.

    Google Scholar 

  • Srinivas, N.,& Deb, K. (1994). Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.

    Google Scholar 

  • Tavakkoli-Moghaddam, R., Safari, J.,& Sassani, F. (2008). Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliability Engineering& System Safety, 93, 550–556.

    Google Scholar 

  • Tian, Z., Lin, D.,& Wu, B. (2012). Condition based maintenance optimization considering multiple objectives. Journal of Intelligent Manufacturing, 23(2), 333–340.

    Google Scholar 

  • Tsai, Y. T., Wang, K. S.,& Teng, H. Y. (2001). Optimizing preventive maintenance for mechanical components using genetic algorithms. Reliability Engineering& System Safety, 74, 89–97.

    Google Scholar 

  • Tsai, Y. T., Wang, K. S.,& Tsai, L. C. (2004). A study of availability-centered preventive maintenance for multi-component systems. Reliability Engineering& System Safety, 261–270.

  • Wang, H. Z. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139, 469–489.

    Article  Google Scholar 

  • Wang, C. H.,& Li, C. H. (2011). Optimization of an established multi-objective delivering problem by an improved hybrid algorithm. Expert Systems with Applications, 38, 4361–4367.

    Article  Google Scholar 

  • Wang, C. H., Lu, J. Z.,& Wu, C. J. (2009). Optimization of a multi-objective transportation model during War for military. The Journal of Chung Cheng Institute of Technology, 37(2), 10–29.

    Google Scholar 

  • Yamachi, H., Tsujimura, Y., Kambayashi, Y.,& Yamamoto, H. (2006). Multi-objective genetic algorithm for solving N-version program design problem. Reliability Engineering& System Safety, 91, 1083–1094.

    Google Scholar 

  • Zequeira, R. I.,& Bérenguer, C. (2006). Periodic imperfect preventive maintenance with two categories of competing failure modes. Reliability Engineering& System Safety, 91, 460–468.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Power Vehicle and Systems Engineering, Chung Cheng Institute of Technology, National Defense University, No.75, Shiyuan Rd., Daxi Township, Tauyuan County, 33551, Taiwan (R.O.C)

    Chung-Ho Wang & Sheng-Wang Tsai

Authors
  1. Chung-Ho Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng-Wang Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sheng-Wang Tsai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, CH., Tsai, SW. Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm. J Intell Manuf 25, 603–616 (2014). https://doi.org/10.1007/s10845-012-0708-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0708-8

Keywords

Search

Navigation