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Multi-attribute Utility Theory analysis for burn-in processes combined with replacement

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Warianty tytułu
PL
Analiza połączonych procesów sztucznego starzenia i wymiany prowadzona w oparciu o wieloatrybutową teorię użyteczności
Języki publikacji
EN
Abstrakty
EN
Components from a heterogeneous population may result in non-well behaviour in the failure rate function. This paper considers a population of components that consists of two different sub-populations: a population of weak components and a population of strong components. This component heterogeneity is treated using a mixture distribution for the components’ lifetimes. This mixture models two distinct behaviours: a short characteristic lifetime for the weak components and a long characteristic lifetime for the strong components. Simple policies may not be effective to address the distinct behaviours of failures for these components. Thus, combined preventive replacement and a burn-in procedure based on a multi-criteria perspective are proposed in order to suitably integrate the different objectives from the burn-in and preventive replacement procedures, taking into account the preferences of the decision-maker. We consider the cost and the mean residual life as the criteria of the proposed model. Multi-attribute Utility Theory (MAUT) allows alternatives that are more aligned with the preferences of the decision-maker to be developed.
PL
Elementy składowe tworzące niejednorodną populację mogą prowadzić do nieprawidłowości funkcji intensywności uszkodzeń. W prezentowanej pracy badano populację komponentów składająca się z dwóch różnych subpopulacji: populacji komponentów słabych i populacji komponentów mocnych. Niejednorodność komponentów opisano za pomocą rozkładu mieszanego ich czasu pracy. Rozkład mieszany pozwala modelować dwa różne zachowania: krótki czas pracy charakterystyczny dla słabych elementów i długi czas pracy charakterystyczny dla elementów mocnych. Proste strategie konserwacyjne mogą nie dawać oczekiwanych efektów w przypadku komponentów, które różnią się pod względem charakteru uszkodzeń. Aby odpowiednio powiązać odmienne cele procedur sztucznego starzenia (wygrzewania, docierania) i wymiany profilaktycznej elementów składowych zaproponowano, w oparciu o podejście wielokryterialne, procedurę łączącą sztuczne starzenie i wymianę profilaktyczną, która uwzględnia także preferencje decydenta. Jako kryteria proponowanego modelu rozważano koszty i średnią trwałość resztkową. Wieloatrybutowa teoria użyteczności (MAUT) pozwala na tworzenie alternatyw, które licują z preferencjami osoby odpowiedzialnej za podejmowanie decyzji eksploatacyjnych.
Rocznik
Strony
599--605
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
  • UFPE – Universidade Federal de Pernambuco Departament of Management Engineering Cidade Universitária Rua Av. da Arquitetura, s/no, CEP - 50740-550 Recife, PE, Brazil
  • UFPE – Universidade Federal de Pernambuco Departament of Management Engineering Cidade Universitária Rua Av. da Arquitetura, s/no, CEP - 50740-550 Recife, PE, Brazil
autor
  • UFPE – Universidade Federal de Pernambuco Departament of Management Engineering Cidade Universitária Rua Av. da Arquitetura, s/no, CEP - 50740-550 Recife, PE, Brazil
Bibliografia
  • 1. Baskin, E M. Analysis of burn-in time using the general law of Reliability. Microelectronics Reliability 2002; 42: 67–74, http://dx.doi.org/10.1016/S0026-2714(02)00244-5.
  • 2. Block, H W, Savits, T H. Burn-In. Statistical Science 1997; 12:1-19.
  • 3. Block, H W, Savits, T H, Singh, H. A criterion for burn-in that balances mean residual life and residual variance. Operations Research 2002; 50: 290–296, http://dx.doi.org/10.1287/opre.50.2.290.435.
  • 4. Bebbington, M, Lai, C D, Zitikis, R. Optimum Burn-in Time for a Bathtub Shaped Failure Distribution. Methodology and Computing in Applied Probability 2007; 9:1–20, http://dx.doi.org/10.1007/s11009-006-9001-7.
  • 5. Canfield, R V, Cost Effective Burn-In and Replacement Times. IEEE Transactions on Reliability 1975; 24 : 154-156, http://dx.doi.org/10.1109/TR.1975.5215124.
  • 6. Cavalcante, C A V, Scarf, P A, de Almeida, A T. A study of a two-phase inspection policy for a preparedness system with a defective state and heterogeneous lifetime. Reliability Engineering & Systems Safety 2011; 96: 627-635, http://dx.doi.org/10.1016/j.ress.2010.12.004.
  • 7. Cavalcante, C.A.V. A Multicriteria Decision Model for a Combined Burn-in and Replacement Policy. Lecture Notes in Computer Science: Evolutionary Multi-Criterion Optimization. Springer, Berlin 2011; 6576: 579-593, http://dx.doi.org/10.1007/978-3-642-19893-9_40.
  • 8. Cavalcante, C A V, Lopes, R S. Multi-criteria model to support the definition of opportunistic maintenance policy: A study in a cogeneration system. Energy (Oxford) 2015; 80: 32-40. 9 Cavalcante, C A V, Lopes, R S. Opportunistic Maintenance Policy for a System with Hidden Failures: A Multicriteria Approach Applied to an Emergency Diesel Generator. Mathematical Problems in Engineering (Online) 2014, 157282; 1-11, http://dx.doi.org/10.1155/2014/157282.
  • 10. Castro, I T, Alfa A S. Lifetime replacement policy in discrete time for a single unit system. Reliability Engineering and System Safety 2004; 84: 103–111, http://dx.doi.org/10.1016/j.ress.2003.11.005.
  • 11. Cha, J H, Finkelstein, M. Burn-in and the performance quality measures in heterogeneous populations. European Journal of Operational Research 2011; 210: 273-280, http://dx.doi.org/10.1016/j.ejor.2010.09.019.
  • 12. Chang, D S. Optimal burn-in decision for products with an unimodal failure rate function. European Journal of Operational Research 2000; 126: 584–640, http://dx.doi.org/10.1016/S0377-2217(99)00308-2.
  • 13. Chi, D H, Kuo W. Burn-in Optimization under Reliability & Capacity Restrictions. IEEE Transactions on Reliability 1989; 38:193-198, http://dx.doi.org/10.1109/24.31104.
  • 14. Chelst, K, Canbolat, Y B. Value-Added Decision Making for Managers. CRC Press/Taylor & Francis Group, Boca Raton, USA. 2012.
  • 15. Drapella, A, Kosznik, S. Short communication combining preventive replacement and burn-in procedures. Quality Reliability Engineering International 2002; 18: 423-427, http://dx.doi.org/10.1002/qre.482.
  • 16. De Almeida, A T, Ferreira, R J P, Cavalcante, C A V. A review of the use of multicriteria and multi-objective models in maintenance and reliability. IMA Journal of Management Mathematics 2015; 1-23, http://dx.doi.org/10.1093/imaman/dpv010.
  • 17. De Almeida, A T. Multicriteria Model for Selection of Preventive Maintenance Intervals. Quality and Reliability Engineering International 2012; 6: 585-593, http://dx.doi.org/10.1002/qre.1415.
  • 18. Dyer, J S, Fishburn, P C, Steuer R E, Wallenius, J Z S. Multiple Criteria Decision Making, Multiattribute Utility Theory: The Next Ten Years. Management Science 1992; 38: 645-654, http://dx.doi.org/10.1287/mnsc.38.5.645.
  • 19. Golmakani, H R, Fattahipour, F. Optimal replacement policy and inspection interval for condition-based maintenance. International Journal of Production Research 2011; 49: 5153–5167, http://dx.doi.org/10.1080/00207543.2010.505935.
  • 20. Hassan, O. Assessing the Sustainability of a Region in the Light of Composite Indicators. Journal of Environmental Assessment Policy and Management 2008; 10: 51-65, http://dx.doi.org/10.1142/S1464333208002981.
  • 21. Jiang, R, Jardine, A K S. An Optimal Burn-in Preventive-replacement Model Associated with a Mixture distribution. Quality and Reliability Engineering International 2007; 23: 83–93, http://dx.doi.org/10.1002/qre.816.
  • 22. Jack, N, Murthy, D N P. Warranty servicing strategies to improve customer satisfaction, IMA Journal of Management Mathematics 2004; 15: 111-124, http://dx.doi.org/10.1093/imaman/15.2.111.
  • 23. Keeney, R L, Raiffa, H. Decision with Multiple Objectives: Preferences and Value Trade-offs. John Wiley & Sons, 1976.
  • 24. Kuo, W, Kuo, Y. Facing the Headaches of Early Failures: A State-of-the-Art Review of Burn-In Decisions. Proceedings of the IEEE 1983; 71: 1257-1266, http://dx.doi.org/10.1109/PROC.1983.12763.
  • 25. Kim, K N. Optimal burn-in for minimizing cost and multiobjectives. Microelectronics Reliability 1998; 38: 1577-1583, http://dx.doi.org/10.1016/S0026-2714(98)00031-6.
  • 26. Kim, T, Kwak, S, Yoo, S. Applying Multi-Attribute Utility Theory to Decision Making in Environmental Planning: A Case Study of the Electric Utility in Korea. Journal of Environmental Planning and Management 1998; 41: 597-609, http://dx.doi.org/10.1080/09640569811470.
  • 27. Lillo, E. Note on relations between criteria for ageing. Reliability Engineering and System Safety 2000; 67:129–133, http://dx.doi.org/10.1016/S0951-8320(99)00058-7.
  • 28. Mi, J. Bathtub failure rate and upside-down bathtub mean residual life. IEEE Transactions on Reliability 1995; 44: 388–391, http://dx.doi.org/10.1109/24.406570.
  • 29. Perlstein, D, Jarvis, W H. Mazzuchi, T A. Bayesian calculation of cost optimal burn-in test durations for mixed exponential populations. Reliability Engineering and System Safety 2001; 72: 265-273, http://dx.doi.org/10.1016/S0951-8320(01)00025-4.
  • 30. Russell A. Ogle, S J. Dee, Brenton L. Cox. Resolving inherently safer design conflicts with decision analysis and multi-attribute utility theory. Process Safety and Environmental 2015; 03: 9-14, http://dx.doi.org/10.1016/j.psep.2015.03.009.
  • 31. Tangaraj, V. Rizwam, U. Optimal replacement policies on burn-in process for an alternative repair model. Information and Management Sciences 2001; 12: 43-56.
  • 32. Torrance G W, Boyle M H, Horwood S P. Application of Multi-Attribute Utility Theory to Measure Social Preferences for Health States. Operations Research 1982; 30: 10-43, http://dx.doi.org/10.1287/opre.30.6.1043.
  • 33. Von Neumann J., Morgenstern, O; Theory of Games and Economic Behavior. Princeton University Press, Princeton 1947; 2.
  • 34. Vincke, P; Multicriteria Decision Aid. John Wiley & sons Ltd, 1992.
  • 35. Wu, S; Clements- Croome, D. Burn-in policies for products having dormant states, Reliability Engineering and System Safety 2007; 92:278-285, http://dx.doi.org/10.1016/j.ress.2006.04.003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0fb303e4-5130-44ba-badd-8cb2aea728ce
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