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Complex componential approach for redundancy allocation problem solved by simulation-optimization framework

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Abstract

This article addresses the problem of redundancy and reliability allocation in the operational dimensioning of an automated production system. The aim of this research is to improve the global reliability of the system by allocating alternative components (redundancies) that are associated in parallel with each original component. By considering a complex componential approach that simultaneously evaluates the interrelations among sub-systems, conflicting goals, and variables of different natures, a solution for the problem is proposed through a multi-objective formulation that joins a multi-objective elitist genetic algorithm with a high-level simulation environment also known as simulation optimization framework.

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Notes

  1. The costs of production include: CRAMA—cost of the raw materials applied; CMPOLl—cost of the manpower of the operation of the line; CEEOC—cost of electricity and other combustibles; CPSAT—cost of packaging, storing, and transport

References

  • Azadivar, F. (1992). A tutorial on simulation optimization. In Proceedings of the, (1992). 24th winter simulation conference, pp. 198–204. doi:10.1145/167293.167332.

  • Billiton, R. A., Ronald N. (1992). Reliability evaluation of engineering systems: Concepts and techniques (2nd ed). Springer. ISBN-10 0306440636; ISBN-13 978–0306440632.

  • Chaube, A., Benyoucef, L., & Tiwari, M. K. (2012). An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. Journal of Intelligent Manufacturing, 23(4), 1141–1155. doi:10.1007/s10845-010-0453-9.

  • Coelho, L. S. (2009). Reliability-redundancy optimization by means of a chaotic differential evolution approach. Chaos, Solitons and Fractals, 41(2), 594–602. doi:10.1016/j.chaos.2008.02.028.

  • Coello, C. A., Lamont, G. B. (2004). Applications of multi-objective evolutionary algorithms. Advances in Natural Computation (Vol. 1). World Scientific ublishing. ISBN 981-256-106-4.

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. England: Wiley. ISBN 047187339X, 9780471873396.

  • Deb, K., Pratap, A., & Agarwal, S. (2002). Fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE-Transactions on Evolutionary Computation, 6(2), 182–197. doi:10.1109/4235.996017.

    Google Scholar 

  • Ergott, M. & Gandibleux, X. (2004). Approximate solution methods for multi-objective combinatorial optimization. Sociedad de Estadística e Investigación Operativa. Madrid. Spain TOP Vol. 12. No. 1. 1–90 June. doi:10.1007/BF02578918.

  • Gholami, M., & Zandieh, M. (2009). Integrating simulation and genetic algorithm to schedule d dynamic flexible job shop. Journal of Intelligent Manufacturing, 20(4), 481–498. doi:10.1007/s10845-008-0150-0.

    Article  Google Scholar 

  • Hani, Y., et al. (2008). Simulation base optimization of a train maintenance facility. Journal of Intelligent Manufacturing, 19(3), 293–300. doi:10.1007/s10845-008-0082-8.

    Google Scholar 

  • Jianping, L. I., & Xishen, J. (1997). A new partial bound enumeration technique for solving reliability redundancy optimization. Microelectronics Reliability, 37(2), 237–242. doi:10.1016/S0026-2714(96)00109-6.

    Article  Google Scholar 

  • Khalili-Damghani, K., & Maghsoud, A. (2012). Solving binary-state multi-objective reliability redundancy allocation series-paralell problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA. Reliability Engineering and System Safety, 103, 35–44. doi:10.1016/j.ress.2012.03.006.

  • Kulturel-Konak, S., & Coit, D. W. (2007). Determination of Pruned Pareto sets for the multi-objective system redundancy allocation problem. IEEE Symposium on computational intelligence in multicriteria decision making, pp. 390–394. doi:10.1109/MCDM.2007.369118.

  • Kulturel-Konak, S., Smith, A. E., & Norman, B. A. (2006). Multi-objective tabu search using a multinomial probability mass function. European Journal of Operational Research, 169, 918–931. doi:10.1016/j.ejor.2004.08.026.

    Google Scholar 

  • Kumar, R., Izui, K., Yoshimura, M., & Nishiwaki, S. (2009). Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization. Reliability Engineering and System Safety, 94(4), 891–904. doi:10.016/j.ress.2008.10.002.

  • Kuo, W., Prasad, V. R., Tillman, F. A., & Hwang, C.-L. (2001). Optimal reliability design fundamentals and applications. Cambridge University Press. ISBN 0521781272, 9780521781275.

  • Kuo, W., Zuo, M. J. (2003). Optimal reliability modeling. Principles and applications. New Jersey: Wiley. ISBN 047139761X, 9780471397618.

  • Liang, Y.-C., & Lo, M.-H. (2010). Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm. Journal of Heuristics, 16, 511–535. doi:10.1007/s10732-009-9108-4.

    Article  Google Scholar 

  • Lins, Isis Didier, & Droguett, Enrique López. (2011). Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation. Simulation Modelling Practice and Theory, 19, 362–381. doi:10.1016/j.simpat.2010.07.010.

    Article  Google Scholar 

  • Lisnianski, A., & Levitin, G. (2003). Multi-state system reliability Assessment optimization and application (Vol. 6). Singapore: World Scientific Publishing. Series on Quality reliability and Engineering statistics. ISBN 981-238-306-9.

  • Massim, Y., Yalaoui, F., Chatelet, E., Yalaoui, A., & Zeblah, A. (2012). Efficient immune algorithm for optimal allocation in series-parallel continuous manufacturing systems. Journal of Intelligent Manufacturing, 23(5), 1603–1619. doi:10.1007/s10845-010-0463-7.

    Google Scholar 

  • Mattila, V., Virtanen, K. (2005). A simulation-based optimization model to schedule periodic maintenance of a fleet of aircraft. In Proceedings 2005. European simulation and modeling conference, Porto, Portugal, October 24–26, pp. 479–483.

  • Meedeniya, I., Aleti, A. & Buhnova, B. (2009). Redundancy allocation in automotive systems using multi-objective optimization. Jacobs school UCSD symposium on automotive/avionics systems engineering SAASE 2009. http://www.jacobsschool.ucsd.edu/GordonCenter/g_leadership/l_summer/s_saase/schedule.shtml

  • Mehmet, Çunkaş. (2010). Intelligent design of induction motors by multiobjective fuzzy genetic algorithm. Journal of Intelligent Manufacturing, 21(4), 393–402. doi:10.007/s10845-008-0187-0.

    Article  Google Scholar 

  • Merkuryeva, G., & Napalkova, L. (2008). Development of multi-objective simulation-based genetic algorithm for supply chain cyclic planning and optimization. In International conference 204th EURO mini conference continuous optimization and knowledge-based technologies (EurOPT) May 20.23. Neringa, Lithuania. ISBN 978-9955-28-283-9.

  • Misra, K. B., Sharma, U. (1991). An efficient approach for multiple criteria redundancy optimization problems. Microelectronics Reliability, 31 (2/3), 303–321. doi:10.016/0026-2714(91)90216.

  • Salazar, D., Rocco, C. M., & Galv an, B. J. (2006). Optimization of constrained multiple-objective reliability problems using evolutionary algorithms. Reliability Engineering and System Safety, 91(9), 1057–1070. doi:10.1016/j.ress.2005.11.040.

    Google Scholar 

  • Shelokar, P. S., Jayaraman, V. K., & Kulkami, B. D. (2002). Ant algorithm for single and multiobjective reliability optimization problems. Quality and Reliability Engineering International, 18, 497–514. doi:10.1002/qre.499.

  • Swisher, J. R., Hyden, P. D., Jacobson, S. H., & Schruben, L. W. (2000). A survey of simulation optimization techniques and procedures. In Proceedings of 2000. Winter simulation conference (Vol. 1, No. 10–13, pp. 119–128). Dec 2000. doi:10.1109/WSC.2000.899706.

  • Taboada, H. A. & Coit, D. W. (2008). Development of multiple objective genetic algorithm for solving reliability design allocation problems. In J. Fowler, & S. Masons (Eds), Proceedings of the 2008 industrial engineering research conference. Available at: http://ie.rutgers.edu/resource/research_paper/paper_08-004.pdf.

  • Taboada, H. A., Baheranwala, F., Coit, D. W., & Wattanapongsakorn, N. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering and System Safety, 92, 314–322. doi:10.1016/j.ress.2006.04.014.

  • Taboada, H. A., & Coit, D. W. (2012). A new multiple objective evolutionary algorithm for reliability optimization of series-paralell systems. Applied Evolutionary Computation, 3(2), 1–18. doi:10.4018/jaec.2012040101.

    Article  Google Scholar 

  • Tian, Shigang, & Suo, Ming J. (2006). Redundancy allocation for multi-state systems using physical programming and genetic algorithms. Reliability Engineering and System Safety, 91, 1049–1056. doi:10.1016/j.ress.2005.11.039.

    Article  Google Scholar 

  • Tian, Z., Lin, D., & Bairong, W. (2012). Condition based maintenance optimization considering multiple objectives. Journal of Intelligent Manufacturing, 33(2), 333–340. doi:10.1007/s10845-009-0358-7.

    Google Scholar 

  • Tzu-Chieh, H. & Kuei-Yuan, C. (2011). Uncertaninty quantifications of Pareto optima in multiobjective problems. Journal of Intelligent Manufacturing doi. doi:10.1007/s10845-011-0602-9.

  • Wang, Z., & Chen T., Tang K., Yao X. (2009). A multi-objective approach to redundancy allocation problem in parallel-series systems. IEEE Congress on evolutionary computation—CEC, pp. 582–589. doi:10.1109/CEC.2009.4982998.

  • Zhou, L., Yoke San, W., & Kim Seng, Lee. (2011). A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm. Journal of Intelligent Manufacturing, 22(6), 891–907. doi:10.1007/s10845-009-0365-8.

    Google Scholar 

  • Zio, E. & Zille, V. (2007). Multi-objective optimization of network systems by using ANT algorithms. Quality Technology of Quantitative management. 4(2), 211–224. Available at:http://web.it.nctu.edu.tw/qtqm/qtqmpapers/2007V4N2/2007V4N2_F4.pdf.

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Authors and Affiliations

  1. UTFPR, Curitiba, Brazil

    Carlos Henrique Mariano

  2. UNAM, Mexico City, Mexico

    Carlos Henrique Mariano

  3. ITAM/IIMAS/UNAM/San Angel, Mexico City, Mexico

    Angel Fernando Kuri-Morales

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  1. Carlos Henrique Mariano
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  2. Angel Fernando Kuri-Morales
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Correspondence to Carlos Henrique Mariano.

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Mariano, C.H., Kuri-Morales, A.F. Complex componential approach for redundancy allocation problem solved by simulation-optimization framework. J Intell Manuf 25, 661–680 (2014). https://doi.org/10.1007/s10845-012-0712-z

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  • DOI: https://doi.org/10.1007/s10845-012-0712-z

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