IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v244y2015i2p611-623.html
   My bibliography  Save this article

Genetic algorithms for condition-based maintenance optimization under uncertainty

Author

Listed:
  • Compare, M.
  • Martini, F.
  • Zio, E.

Abstract

This paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GAs), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant.

Suggested Citation

  • Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:611-623
    DOI: 10.1016/j.ejor.2015.01.057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715000776
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2015.01.057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Li, Jingpeng & Kwan, Raymond S. K., 2003. "A fuzzy genetic algorithm for driver scheduling," European Journal of Operational Research, Elsevier, vol. 147(2), pages 334-344, June.
    3. Whitmore, G A, 1970. "Third-Degree Stochastic Dominance," American Economic Review, American Economic Association, vol. 60(3), pages 457-459, June.
    4. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    5. Alsyouf, Imad, 2007. "The role of maintenance in improving companies' productivity and profitability," International Journal of Production Economics, Elsevier, vol. 105(1), pages 70-78, January.
    6. Martorell, S. & Sanchez, A. & Carlos, S., 2007. "A tolerance interval based approach to address uncertainty for RAMS+C optimization," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 408-422.
    7. Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.
    8. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    9. Sanchez, Ana & Carlos, Sofia & Martorell, Sebastian & Villanueva, Jose F., 2009. "Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 22-32.
    10. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
    11. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    12. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    13. Melnikov, Alexander & Smirnov, Ivan, 2012. "Dynamic hedging of conditional value-at-risk," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 182-190.
    14. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, March.
    15. Moura, Márcio das Chagas & Lins, Isis Didier & Droguett, Enrique López & Soares, Rodrigo Ferreira & Pascual, Rodrigo, 2015. "A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 253-265.
    16. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    17. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    18. Massimiliano Giorgio & Maurizio Guida & Gianpaolo Pulcini, 2011. "An age- and state-dependent Markov model for degradation processes," IISE Transactions, Taylor & Francis Journals, vol. 43(9), pages 621-632.
    19. Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    20. Villanueva, J.F. & Sanchez, A.I. & Carlos, S. & Martorell, S., 2008. "Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1830-1841.
    21. Fan, Zhi-Ping & Liu, Yang & Feng, Bo, 2010. "A method for stochastic multiple criteria decision making based on pairwise comparisons of alternatives with random evaluations," European Journal of Operational Research, Elsevier, vol. 207(2), pages 906-915, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Efraim Laksman & Ann-Brith Strömberg & Michael Patriksson, 2020. "The stochastic opportunistic replacement problem, part III: improved bounding procedures," Annals of Operations Research, Springer, vol. 292(2), pages 711-733, September.
    2. Saleh, Ali & Chiachío, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Levitin, Gregory & Xing, Liudong & Xiang, Yanping, 2021. "Optimizing preventive replacement schedule in standby systems with time consuming task transfers," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Bei, Xiaoqiang & Zhu, Xiaoyan & Coit, David W., 2019. "A risk-averse stochastic program for integrated system design and preventive maintenance planning," European Journal of Operational Research, Elsevier, vol. 276(2), pages 536-548.
    5. Syan, Chanan S. & Ramsoobag, Geeta, 2019. "Maintenance applications of multi-criteria optimization: A review," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    6. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2024. "Optimal task aborting and sequencing in time constrained multi-task multi-attempt missions," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    8. Xiao, Hui & Yi, Kunxiang & Liu, Haitao & Kou, Gang, 2021. "Reliability modeling and optimization of a two-dimensional sliding window system," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    10. Martón, I. & Martorell, P. & Mullor, R. & Sánchez, A.I. & Martorell, S., 2016. "Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 151-158.
    11. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    12. Xing, Liudong & Levitin, Gregory, 2018. "Connectivity modeling and optimization of linear consecutively connected systems with repairable connecting elements," European Journal of Operational Research, Elsevier, vol. 264(2), pages 732-741.
    13. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    14. Levitin, Gregory & Finkelstein, Maxim & Xiang, Yanping, 2020. "Optimal aborting rule in multi-attempt missions performed by multicomponent systems," European Journal of Operational Research, Elsevier, vol. 283(1), pages 244-252.
    15. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    16. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    17. Xiaosheng Zhang & Jianqiao Chen & Ben Han & Junxiang Li, 2019. "Multi-mission selective maintenance modelling for multistate systems over a finite time horizon," Journal of Risk and Reliability, , vol. 233(6), pages 1040-1059, December.
    18. Peng, Rui & Liu, Bin & Zhai, Qingqing & Wang, Wenbin, 2019. "Optimal maintenance strategy for systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 624-632.
    19. Chen, Liwei & Gao, Yansan & Dui, Hongyan & Xing, Liudong, 2021. "Importance measure-based maintenance optimization strategy for pod slewing system," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. Chiachío, Manuel & Saleh, Ali & Naybour, Susannah & Chiachío, Juan & Andrews, John, 2022. "Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    21. Radim Briš & Nuong Thi Thuy Tran, 2023. "Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Compare, Michele & Bellani, Luca & Zio, Enrico, 2019. "Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 164-180.
    2. Michele Compare & Francesco Di Maio & Enrico Zio & Fausto Carlevaro & Sara Mattafirri, 2016. "Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach," Journal of Risk and Reliability, , vol. 230(5), pages 502-511, October.
    3. Compare, Michele & Bellani, Luca & Zio, Enrico, 2017. "Reliability model of a component equipped with PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 4-11.
    4. Shiyu Chen & Wei Wang & Enrico Zio, 2021. "A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains," Energies, MDPI, vol. 14(9), pages 1-27, May.
    5. Naseri, Masoud & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2016. "Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 66-82.
    6. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    7. Michele Compare & Luca Bellani & Enrico Zio, 2017. "Availability Model of a PHM-Equipped Component," Post-Print hal-01652232, HAL.
    8. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.
    9. Mena, Rodrigo & Hennebel, Martin & Li, Yan-Fu & Zio, Enrico, 2016. "A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems," Energy, Elsevier, vol. 106(C), pages 712-727.
    10. Di Maio, Francesco & Pettorossi, Chiara & Zio, Enrico, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.
    12. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.
    13. Penttinen, Jussi-Pekka & Niemi, Arto & Gutleber, Johannes & Koskinen, Kari T. & Coatanéa, Eric & Laitinen, Jouko, 2019. "An open modelling approach for availability and reliability of systems," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 387-399.
    14. Rocco, Claudio M. & Moronta, José & Ramirez-Marquez, José E. & Barker, Kash, 2017. "Effects of multi-state links in network community detection," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 46-56.
    15. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.
    16. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    17. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
    18. Martin Folch-Calvo & Francisco Brocal-Fernández & Cristina González-Gaya & Miguel A. Sebastián, 2020. "Analysis and Characterization of Risk Methodologies Applied to Industrial Parks," Sustainability, MDPI, vol. 12(18), pages 1-35, September.
    19. Cadini, F. & Gioletta, A., 2016. "A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 15-27.
    20. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:611-623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.