Ensuring reliable operation and maximum the output is crucial for energy production systems. Traditional time-based availability (TBA) warranty policies often overlook some factors, such as energy loss and the experience gained during the maintenance activities. In this paper, an innovative warranty policy which focuses on the energy-based availability (EBA) is proposed, where imperfect preventive maintenance (IPM) and minimal repair (MR) are taken into account, and hybrid hazard rate model is adopted to describe the effect of preventive maintenance. In addition, the learning-forgetting effect during the maintenance is considered. On this basis, six types of single-objective and multi-objective models are established, and they are solved by genetic algorithm (GA) and non-dominated sorting genetic algorithm-II (NSGA-II), respectively. To illustrate the effectiveness of the proposed warranty policy, a numerical case of wind turbine gearbox is conducted. The results show that the proposed EBA warranty policy can gain around 0.29% energy more than TBA policy. Compared to single-objective models, multi-objective models can provide more selectable maintenance options. Additionally, sensitivity analysis indicates that by considering the learning-forgetting effect, the gearbox can achieve higher EBA and lower warranty cost.
MurthyDNP. Product warranty and reliability. Ann Oper Res2006; 143(1): 133–146.
2.
MurthyDNPBlischkeWR. Warranty management and product manufacture. London: Springer Science & Business Media, 2005.
3.
IskandarBPSa’idahNFPasaribuUS, et al. Warranty and maintenance service contracts for repairable products. Alex Eng J2022; 61(12): 10819–10835.
4.
ZhengRSuC.A flexible two-dimensional basic warranty policy with two continuous warranty regions. Qual Reliab Eng Int2020; 36(6): 2003–2018.
5.
YazdekhastiAMehrjardiYZZareHK, et al. The integrated supply chain of after-sales services model: a multi-objective scatter search optimization approach. Int J Eng2018; 31(3): 422–431.
6.
ArnoldRChukovaSHayakawaY, et al. Mean and variance of an alternating geometric process: An application in warranty cost analysis. Qual Reliab Eng Int2022, 38(6): 2968–2985.
7.
SalmasniaAHatamiAMalekiMR.Joint optimization of two-dimensional preventive maintenance parameters, technology level and product price under continuous usage rate and agent warranty services. Int J Model Simul2023: 1–20.
8.
AfsahiMShafieeM.A stochastic simulation-optimization model for base-warranty and extended warranty decision-making of under- and out-of-warranty products. Reliab Eng Syst Saf2020; 197: 106772.
9.
SuCChengLF.An availability-based warranty policy considering preventive maintenance and learning effects. Proc IMechE, Part O: J Risk and Reliability2018; 232(6): 576–586.
10.
ZhengRZhangYZGuLD.Optimal post-warranty maintenance contracts for wind turbines considering availability. Int J Green Energy2020; 17(7): 373–381.
11.
ConroyNDeaneJPGallachóirBPÓ. Wind turbine availability: should it be time or energy based? A case study in Ireland. Renew Energy2011; 36(11): 2967–2971.
12.
Moradi-ShahrbabakZTabeshAYousefiGR.Economical design of utility-scale photovoltaic power plants with optimum availability. IEEE Trans Ind Electron2013; 61(7): 3399–3406.
13.
HuntKBlekickiACalleryR.Availability of utility-scale photovoltaic power plants. In: 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), New Orleans, LA, 14–19 June 2015, pp.1–3. New York: IEEE.
14.
ShafieeMChukovaS.Maintenance models in warranty: A literature review. Eur J Oper Res2013; 229(3): 561–572.
15.
AhmadRKamaruddinS.An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng2012; 63(1): 135–149.
16.
LiXYJiaYXWangP, et al. Renewable warranty policy for multiple-failure-mode product considering different maintenance options. Eksploat Niezawodn2015; 17(4): 551–560.
17.
DoyenLGaudoinO.Classes of imperfect repair models based on reduction of failure intensity or virtual age. Reliab Eng Syst Saf2004; 84(1): 45–56.
18.
WuSZuoMJ.Linear and nonlinear preventive maintenance models. IEEE Trans Reliab2010; 59(1): 242–249.
19.
LinDZuoMJYamRCM. General sequential imperfect preventive maintenance models. Int J Reliab Qual Saf Eng2000; 7(3): 253–266.
20.
KhatabA.Hybrid hazard rate model for imperfect preventive maintenance of systems subject to random deterioration. J Intell Manuf2015; 26(3): 601–608.
21.
PandeyMZuoMJMoghaddassR.Selective maintenance scheduling over a finite planning horizon. Proc IMechE, Part: O J Risk and Reliability2016; 230(2): 162–177.
22.
WuHZhengHM.Single-machine scheduling with periodic maintenance and learning effect. Sci. Rep. 2023; 13(1): 12586.
23.
AzzouzAEnnigrouMBen SaidL.Scheduling problems under learning effects: Classification and cartography. Int J Prod Res2018; 56(4): 1642–1661.
24.
GlockCHGrosseEHJaberMY, et al. Applications of learning curves in production and operations management: a systematic literature review. Comput Ind Eng2019; 131: 422–441.
25.
ThomassenGVan PasselSDewulfJ.A review on learning effects in prospective technology assessment. Renew Sust Energ Rev2020; 130: 109937.
26.
WeiZJWangLYZhangL, et al. Single-machine maintenance activity scheduling with convex resource constraints and learning effects. Mathematics2023; 11(16): 3536.
27.
TarakciHTangKTeyarachakulS.Learning and forgetting effects on maintenance outsourcing. IIE Trans2013; 45(4): 449–463.
28.
TarakciH.Two types of learning effects on maintenance activities. Int J Prod Res2016; 54(6): 1721–1734.
29.
HuberSSpinlerS.Pricing of full-service repair contracts with learning, optimized maintenance and information asymmetry. Decis Sci2014; 45(4): 791–815.
30.
ArgoteL.Group and organizational learning-curves – individual, system and environmental components. Br J Soc Psychol1993; 32: 31–51.
31.
SzwarcEBocewiczGGolinska-DawsonP, et al. Proactive operations management: staff allocation with competence maintenance constraints. Sustainability2023; 15(3): 1949.
32.
PanESWangGNXiLF, et al. Single-machine group scheduling problem considering learning, forgetting effects and preventive maintenance. Int J Prod Res2014; 52(19): 5690–5704.
33.
LiaoWZZhangXFJiangM.Multi-objective group scheduling optimization integrated with preventive maintenance. Eng Optimiz2017; 49(11): 1890–1904.
34.
ZhengRZhouYFZhangYZ.Optimal preventive maintenance for wind turbines considering the effects of wind speed. Wind Energy2020; 23(11): 1987–2003.
35.
YangLPengRLIGY, et al. Operations management of wind farms integrating multiple impacts of wind conditions and resource constraints. Energ Convers Manage2020; 205: 112162.
LiuG HChenS KHoTK, et al. Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy. Reliab Eng Syst Saf2022: 108572.
38.
GonzaloAPBenmessaoudTEntezamiM, et al. Optimal maintenance management of offshore wind turbines by minimizing the costs. Sustain Energy Technol Assess2022; 52: 102230.
39.
ChenXHAnYJZhangZY, et al. An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. J Manuf Syst2020; 54: 227–241.
