REFERENCES

1. Hong Y, Duan Y, Meeker WQ, Stanley DL, Gu X. Statistical methods for degradation data with dynamic covariates information and an application to outdoor weathering data. Technometrics 2015;57:180-93.

2. Diallo C, Venkatadri U, Khatab A, Bhakthavatchalam S. State of the art review of quality, reliability and maintenance issues in closed-loop supply chains with remanufacturing. Int J Prod Res 2017;55:1277-96.

3. Zhang D, Zhang Y. Dynamic decision-making for reliability and maintenance analysis of manufacturing systems based on failure effects. Enterp Inf Syst 2017;11:1228-42.

4. Gandoman FH, Jaguemont J, Goutam S, et al. Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: basics, progress, and challenges. Appl Energy 2019;251:113343.

5. Zhao X, Gaudoin O, Doyen L, Xie M. Optimal inspection and replacement policy based on experimental degradation data with covariates. IISE Trans 2019;51:322-36.

6. Lobato FS, da Silva MA, Cavalini Jr AA, Steffen Jr V. Reliability-based robust multi-objective optimization applied to engineering system design. Eng Optim 2020;52:1-21.

7. Liu L, Li X, Jiang T, Sun F. Utilizing accelerated degradation and field data for life prediction of highly reliable products. Qual Reliab Eng Int 2016;32:2281-97.

8. Collins DH, Freels JK, Huzurbazar AV, Warr RL, Weaver BP. Accelerated test methods for reliability prediction. J Qual Technol 2013;45:244-59.

9. Baladeh AE, Zio E. A two-stage stochastic programming model of component test plan and redundancy allocation for system reliability optimization. IEEE Trans Reliab 2021;70:99-109.

10. Mellal MA, Al-Dahidi S, Williams EJ. System reliability optimization with heterogeneous components using hosted cuckoo optimization algorithm. Reliab Eng Syst Saf 2020;203:107110.

11. Coit DW, Zio E. The evolution of system reliability optimization. Reliab Eng Syst Saf 2019;192:106259.

12. Li X, Zhang W, Ding Q. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 2019;182:208-18.

13. Peng W, Hong L, Ye Z. Degradation-based reliability modeling of complex systems in dynamic environments. Singapore: Springer; 2017; pp. 81-103.

14. Li C, Wang X, Li L, Xie M, Wang X. On dynamically monitoring aggregate warranty claims for early detection of reliability problems. IISE Trans 2020;52:568-87.

15. Gan S, Hu H, Coit DW. Maintenance optimization considering the mutual dependence of the environment and system with decreasing effects of imperfect maintenance. Reliab Eng Syst Saf 2023;235:109202.

16. Friederich J, Lazarova-Molnar S. Reliability assessment of manufacturing systems: a comprehensive overview, challenges and opportunities. J Manuf Syst 2024;72:38-58.

17. Meeker WQ, Escobar LA. A review of recent research and current issues in accelerated testing. Int Stat Rev 1993;61:147.

18. Limon S, Yadav OP, Liao H. A literature review on planning and analysis of accelerated testing for reliability assessment. Qual Reliab Eng Int 2017;33:2361-83.

19. Zhao X, Xu J, Liu B. Accelerated degradation tests planning with competing failure modes. IEEE Trans Reliab 2018;67:142-55.

20. Meeker WQ, Escobar LA, Hong Y. Using accelerated life tests results to predict product field reliability. Technometrics 2009;51:146-61.

21. Meeker WQ, Escobar LA, Lu CJ. Accelerated degradation tests: modeling and analysis. Technometrics 1998;40:89.

22. Ye Z, Chen L, Tang LC, Xie M. Accelerated degradation test planning using the inverse gaussian process. IEEE Trans Reliab 2014;63:750-63.

23. Ghaderi A, Hassani H, Khodaygan S. A Bayesian-reliability based multi-objective optimization for tolerance design of mechanical assemblies. Reliab Eng Syst Saf 2021;213:107748.

24. Song J, Cui Y, Wei P, Valdebenito MA, Zhang W. Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis. Reliab Eng Syst Saf 2024;241:109613.

25. Fang G, Pan R, Stufken J. Optimal setting of test conditions and allocation of test units for accelerated degradation tests with two stress variables. IEEE Trans Reliab 2021;70:1096-111.

26. Zhao X, Chen P, Lv S, He Z. Reliability testing for product return prediction. Eur J Oper Res 2023;304:1349-63.

27. Etemadi AH, Fotuhi-Firuzabad M. Design and routine test optimization of modern protection systems with reliability and economic constraints. IEEE Trans Power Deliv 2012;27:271-8.

28. Wang R, Xu J, Zhang W, Gao J, Li Y, Chen F. Reliability analysis of complex electromechanical systems: state of the art, challenges, and prospects. Qual Reliab Eng Int 2022;38:3935-69.

29. He K, Sun Q, Xie M, Kuo W. Sequential Bayesian planning for accelerated degradation tests considering sensor degradation. IEEE Trans Reliab 2023;72:964-74.

30. Lee I, Hong Y, Tseng S, Dasgupta T. Sequential Bayesian design for accelerated life tests. Technometrics 2018;60:472-83.

31. Sutton RS, Barto AG. Reinforcement learning: an introduction. MIT Press; 2018. Available from: https://books.google.com/books?hl=zh-CN&lr=&id=uWV0DwAAQBAJ&oi=fnd&pg=PR7&dq=Sutton,+R.+S.+%26+Barto,+A.+G.+Reinforcement+Learning:+An+Introduction+(MIT+Press,+2018).&ots=mjqHm-YYo0&sig=qXB9jA_4jYcn0-4JysHj08ubr1I#v=onepage&q=Sutton%2C%20R.%20S.%20%26%20Barto%2C%20A.%20G.%20Reinforcement%20Learning%3A%20An%20Introduction%20(MIT%20Press%2C%202018).&f=false [Last accessed on 30 Nov 2024]

32. Zhang Z, Yang L, Xu Y, Zhu R, Cao Y. A novel reliability redundancy allocation problem formulation for complex systems. Reliab Eng Syst Saf 2023;239:109471.

33. Li J, Huang Y, Li Y, Wang S. Redundancy allocation under state-dependent distributional uncertainty of component lifetimes. Prod Oper Manag 2023;32:930-50.

34. Wang S, Li YF. Distributionally robust design for redundancy allocation. INFORMS J Comput 2020;32:620-40.

35. Reihaneh M, Abouei Ardakan M, Eskandarpour M. An exact algorithm for the redundancy allocation problem with heterogeneous components under the mixed redundancy strategy. Eur J Oper Res 2022;297:1112-25.

36. Peiravi A, Nourelfath M, Zanjani MK. Universal redundancy strategy for system reliability optimization. Reliab Eng Syst Saf 2022;225:108576.

37. Meng Z, Zhang Z, Zhou H. A novel experimental data-driven exponential convex model for reliability assessment with uncertain-but-bounded parameters. Appl Math Model 2020;77:773-87.

38. Murthy DNP, Xie M, Jiang R. Weibull models. Wiley; 2004.

39. Hajiha M, Liu X, Hong Y. Degradation under dynamic operating conditions: modeling, competing processes and applications. J Qual Technol 2021;53:347-68.

40. Singpurwalla ND. Survival in dynamic environments. Stat Sci 1995;10:86-103.

41. Eryilmaz S, Rıza Bozbulut A. An algorithmic approach for the dynamic reliability analysis of non-repairable multi-state weighted k-out-of-n:G system. Reliab Eng Syst Saf 2014;131:61-5.

42. Luo C, Shen L, Xu A. Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints. Reliab Eng Syst Saf 2022;218:108136.

43. Ye Z, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Appl Stoch Models Bus & Ind 2015;31:16-32.

44. Zhang S, Zhai Q, Shi X, Liu X. A wiener process model with dynamic covariate for degradation modeling and remaining useful life prediction. IEEE Trans Reliab 2023;72:214-23.

45. Xu Z, Saleh JH. Machine learning for reliability engineering and safety applications: review of current status and future opportunities. Reliab Eng Syst Saf 2021;211:107530.

46. Song S, Coit DW, Feng Q, Peng H. Reliability analysis for multi-component systems subject to multiple dependent competing failure processes. IEEE Trans Reliab 2014;63:331-45.

47. Han X, Ouyang M, Lu L, Li J, Zheng Y, Li Z. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification. J Power Sources 2014;251:38-54.

48. Fan M, Zeng Z, Zio E, Kang R. Modeling dependent competing failure processes with degradation-shock dependence. Reliab Eng Syst Saf 2017;165:422-30.

49. Wu B, Ding D. A gamma process based model for systems subject to multiple dependent competing failure processes under Markovian environments. Reliab Eng Syst Saf 2022;217:108112.

50. Zhou H, Li Y. Optimal replacement in a proportional hazards model with cumulative and dependent risks. Comput Ind Eng 2023;176:108930.

51. Zhai Q, Ye Z, Li C, Revie M, Dunson DB. Modeling recurrent failures on large directed networks. J Am Stat Assoc 2024:1-15.

52. de Jonge B, Scarf PA. A review on maintenance optimization. Eur J Oper Res 2020;285:805-24.

53. Hu J, Huang Y, Shen L. Maintenance optimization of a two-component series system considering masked causes of failure. Qual Reliab Eng Int 2024;40:388-405.

54. Sun Q, Chen P, Wang X, Ye Z. Robust condition-based production and maintenance planning for degradation management. Prod Oper Manag 2023;32:3951-67.

55. Uit Het Broek MA, Teunter RH, de Jonge B, Veldman J. Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency. Eur J Oper Res 2021;295:1119-31.

56. Puterman ML. Chapter 8 Markov decision processes. Elsevier; 1990; pp. 331-434.

57. Wiesemann W, Kuhn D, Rustem B. Robust Markov decision processes. Math Oper Res 2013;38:153-83.

58. Deep A, Zhou S, Veeramani D, Chen Y. Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations. Eur J Oper Res 2023;311:533-44.

59. Drent C, Drent M, Arts J, Kapodistria S. Real-time integrated learning and decision making for cumulative shock degradation. Manuf Serv Oper Manag 2023;25:235-53.

60. Mahmoodi S, Hamed Ranjkesh S, Zhao YQ. Condition-based maintenance policies for a multi-unit deteriorating system subject to shocks in a semi-Markov operating environment. Qual Eng 2020;32:286-97.

61. Arcieri G, Hoelzl C, Schwery O, Straub D, Papakonstantinou KG, Chatzi E. POMDP inference and robust solution via deep reinforcement learning: an application to railway optimal maintenance. Mach Learn 2024;113:7967-95.

62. González-Prida V, Crespo Márquez A. A framework for warranty management in industrial assets. Comput Ind 2012;63:960-71.

63. Zheng R, Zhou Y, Gu L, Zhang Z. Joint optimization of lot sizing and condition-based maintenance for a production system using the proportional hazards model. Comput Ind Eng 2021;154:107157.

64. Luo Y, Zhao X, Liu B, He S. Condition-based maintenance policy for systems under dynamic environment. Reliab Eng Syst Saf 2024;246:110072.

65. Farahani A, Tohidi H. Integrated optimization of quality and maintenance: a literature review. Comput Ind Eng 2021;151:106924.

66. Van Horenbeek A, Buré J, Cattrysse D, Pintelon L, Vansteenwegen P. Joint maintenance and inventory optimization systems: A review. Int J Prod Econ 2013;143:499-508.

67. Paraschos PD, Koulinas GK, Koulouriotis DE. Reinforcement learning for combined production-maintenance and quality control of a manufacturing system with deterioration failures. J Manuf Syst 2020;56:470-83.

68. Zheng M, Ye H, Wang D, Pan E. Joint decisions of components replacement and spare parts ordering considering different supplied product quality. IEEE Trans Automat Sci Eng 2024;21:1952-64.

69. Xu J, Zhao X, Liu B. A risk-aware maintenance model based on a constrained Markov decision process. IISE Trans 2022;54:1072-83.

70. Ogunfowora O, Najjaran H. Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization. J Manuf Syst 2023;70:244-63.