退化系统的质量控制、状态维修与备件订购联合策略优化

doi:10.16183/j.cnki.jsjtu.2022.356

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (3): 361-370.doi: 10.16183/j.cnki.jsjtu.2022.356

退化系统的质量控制、状态维修与备件订购联合策略优化

韩梦莹¹, 马书刚¹(), 杨建华²^,³, 李伟¹, 马志超⁴

1.河北经贸大学商学院,石家庄 050061
2.北京科技大学经济管理学院,北京 100083
3.北京企业低碳运营战略研究基地,北京 100083
4.河北工程大学管理工程与商学院,河北邯郸 056038

收稿日期:2022-09-13 修回日期:2022-11-21 接受日期:2022-12-21 出版日期:2024-03-28 发布日期:2024-03-28
通讯作者: 马书刚,副教授,电话(Tel.):0311-87655622;E-mail:mashugang@126.com.
作者简介:韩梦莹(1990-),讲师,现主要从事维修决策与优化、预测与健康管理研究.
基金资助:
河北省自然科学基金(G2022207003);河北省社会科学基金(HB21GL025);河北省高等学校人文社会科学研究(BJS2023029);河北省高等学校人文社会科学研究(BJ2021070)

Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System

HAN Mengying¹, MA Shugang¹(), YANG Jianhua²^,³, LI Wei¹, MA Zhichao⁴

1. School of Business, Hebei University of Economics and Business, Shijiazhuang 050061, China
2. School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
3. Beijing Institute of Low Carbon Operations Strategy for Enterprises, Beijing 100083, China
4. School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, Hebei, China

Received:2022-09-13 Revised:2022-11-21 Accepted:2022-12-21 Online:2024-03-28 Published:2024-03-28

摘要/Abstract

摘要：

针对关键生产系统退化过程中出现3种状态的问题,提出基于延迟时间理论的质量控制、状态维修与备件订购联合策略.首先,考虑系统退化状态与产品质量之间的函数关系,设计了在系统退化初期检查系统状态,而在退化后期检查产品质量的两阶段检测策略.其次,根据状态检测信息、产品质量信息及故障信息选择应采取的维修活动,结合系统更新时备件所处的状态确定检测周期内各种可能的事件,构建有限时域内的平均费用率模型.然后,基于离散事件仿真和响应曲面法设计优化算法以快速近似求解模型.最后,通过算例分析部分将所提联合策略与对比模型策略进行比较,验证了所提联合策略的有效性与可行性.

关键词: 质量控制, 状态维修, 备件订购, 延迟时间, 仿真优化

Abstract:

A joint policy of quality control, condition-based maintenance and spare ordering is proposed for a degradation system subject to the delay time concept. First, considering the fact that product quality is largely dependent on system state, a two-stage inspection policy is proposed, in which the system state is detected during the initial deterioration process, but the product quality is checked after the defective state is found by an inspection. Then, based on the condition inspection information, quality information and failure information, the corresponding maintenance activity is chosen. Combining the state of the spare part when the system replacement is required, all possible events during an inspection interval are discussed and then a mathematical model of average cost rate is established. Afterwards, a simulation-based optimization approach coupling discrete event simulation and response surface methodology is devised to obtain a near optimal joint policy. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed policy by comparing it with the comparative policy.

Key words: quality control, condition-based maintenance, spare ordering, delay time, simulation-based optimization

中图分类号:

TH17
O213

韩梦莹, 马书刚, 杨建华, 李伟, 马志超. 退化系统的质量控制、状态维修与备件订购联合策略优化[J]. 上海交通大学学报, 2024, 58(3): 361-370.

HAN Mengying, MA Shugang, YANG Jianhua, LI Wei, MA Zhichao. Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 361-370.

图/表 9

图1

表1

图2

图3

图4

表2

模型1方差分析(ANOVA)相关结果

因素	SS	d.f	MS	F-value	p-value
Model 1	325.26	9	36.14	40.51	< 0.000 1
T	36.69	1	36.69	41.13	0.000 4
M	75.89	1	75.89	85.08	< 0.000 1
R_P	0.033 9	1	0.033 9	0.038	0.851 1
TM	58.81	1	58.81	65.92	< 0.000 1
TR_P	0.049 8	1	0.049 8	0.055 8	0.82
MR_P	2.39	1	2.39	2.68	0.145 9
T²	61.81	1	61.81	69.29	< 0.000 1
M²	50.12	1	50.12	56.19	0.000 1
$R P 2$	24.23	1	24.23	27.16	0.001 2
Lack of Fit	4.28	3	1.43	2.9	0.164 8
Pure Error	1.96	4	0.491 2
Cor Total	331.5	16

表2

图5

表3

模型2方差分析(ANOVA)相关结果

因素	SS	d.f	MS	F-value	p-value
Model 2	534.43	5	106.89	123.49	< 0.000 1
T	17.52	1	17.52	20.24	0.002 8
R_P	256.46	1	256.46	296.29	< 0.000 1
TR_P	35.12	1	35.12	40.58	0.000 4
T²	24.46	1	24.46	28.26	0.001 1
$R P 2$	125.89	1	125.89	145.44	< 0.000 1
Lack of Fit	4.47	3	1.49	3.75	0.117 3
Pure Error	1.59	4	0.397 7
Cor Total	540.49	12

表3

表4

CF敏感性分析结果

C_F	(T^, M^, $R P *$ )	c(T^, M^, $R P *$ )	C(T^, M^, $R P *$ )	(T^, $R P $ )	c(T^, $R P $ )	C(T^, $R P $ )
3 100	(14, 2, 0.078)	90.381 0	91.257 6	(13, 0.076)	91.370 8	92.072 8
3 700	(14, 2, 0.076)	95.407 6	96.175 2	(13, 0.072)	96.708 8	97.084 8
4 300	(13, 2, 0.075)	100.021 1	100.518 2	(12, 0.069)	101.351 0	101.962 3
4 900	(13, 2, 0.065)	105.338 9	105.760 6	(12, 0.066)	106.364 0	106.226 3
5 500	(11, 3, 0.058)	109.960 4	110.274 5	(12, 0.06)	110.598 0	110.331 6

表4

参考文献 18

[1]	ALASWAD S, XIANG Y. A review on condition-based maintenance optimization models for stochastically deteriorating system[J]. Reliability Engineering & System Safety, 2017, 157: 54-63. doi: 10.1016/j.ress.2016.08.009 URL
[2]	AZIMPOOR S, TAGHIPOUR S. Joint inspection and product quality optimization for a system with delayed failure[J]. Reliability Engineering & System Safety, 2021, 215: 107793. doi: 10.1016/j.ress.2021.107793 URL
[3]	WANG H Y, WANG W B, PENG R. A two-phase inspection model for a single component system with three-stage degradation[J]. Reliability Engineering & System Safety, 2017, 158(2): 31-40. doi: 10.1016/j.ress.2016.10.005 URL
[4]	ALBERTI A R, FERREIRA NETO W A, CAVALCANTE C A V, et al. Modelling a flexible two-phase inspection-maintenance policy for safety-critical systems considering revised and non-revised inspections[J]. Reliability Engineering & System Safety, 2022, 221: 108309. doi: 10.1016/j.ress.2021.108309 URL
[5]	刘勤明, 李永朋, 叶春明. 基于三阶段时间延迟模型的设备预防维修策略[J]. 控制与决策, 2020, 35(7): 1780-1786.
	LIU Qinming, LI Yongpeng, YE Chunming. Preventive maintenance plan of equipment based on three-stage time delay model[J]. Control and Decision, 2020, 35(7): 1780-1786.
[6]	YANG L, YE Z S, LEE C G, et al. A two-phase preventive maintenance policy considering imperfect repair and postponed replacement[J]. European Journal of Operational Research, 2019, 274(3): 966-977. doi: 10.1016/j.ejor.2018.10.049 URL
[7]	ZHANG F X, SHEN J Y, LIAO H T, et al. Optimal preventive maintenance policy for a system subject to two-phase imperfect inspections[J]. Reliability Engineering & System Safety, 2021, 205: 107254. doi: 10.1016/j.ress.2020.107254 URL
[8]	杨志远, 赵建民, 程中华, 等. 多元退化系统维修与备件订购策略优化模型[J]. 上海交通大学学报, 2021, 55(7): 858-867. doi: 10.16183/j.cnki.jsjtu.2019.221
	YANG Zhiyuan, ZHAO Jianmin, CHENG Zhonghua, et al. Optimization model of maintenance and spare parts ordering policy in multivariate degradation system[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 858-867.
[9]	YAN T, LEI Y G, WANG B, et al. Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions[J]. Reliability Engineering & System Safety, 2020, 202: 106994. doi: 10.1016/j.ress.2020.106994 URL
[10]	PATER I D, MITICI M. Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components[J]. Reliability Engineering & System Safety, 2021, 214: 107761. doi: 10.1016/j.ress.2021.107761 URL
[11]	赵斐, 刘学娟. 考虑不完美维修的定期检测与备件策略联合优化[J]. 系统工程理论与实践, 2017, 37(12): 3201-3214. doi: 10.12011/1000-6788(2017)12-3201-14
	ZHAO Fei, LIU Xuejuan. Joint optimization of periodical inspection and spare parts policies considering imperfect maintenance[J]. Systems Engineering-Theory & Practice, 2017, 37(12): 3201-3214.
[12]	韩梦莹, 杨建华. 考虑不完美检测的两阶段点检与备件订购策略联合优化[J]. 运筹与管理, 2021, 30(3): 27-34. doi: 10.12005/orms.2021.0071
	HAN Mengying, YANG Jianhua. Joint optimization of two-phase inspection and spare parts ordering policies considering imperfect inspection[J]. Operations Research and Management Science, 2021, 30(3): 27-34. doi: 10.12005/orms.2021.0071
[13]	ZHAO F, XIE F F, SHI C H, et al. A joint inspection-based preventive maintenance and spare ordering optimization policy using a three-stage failure process[J]. Complexity, 2017, 2017: 1-19.
[14]	ZHAO F. LIU X J. PENG R, et al. Joint optimization of inspection and spare ordering policy with multi-level defect information[J]. Computers and Industrial Engineering, 2020, 139: 106205. doi: 10.1016/j.cie.2019.106205 URL
[15]	ZHAO Y X, HE Y H, ZHOU D, et al. Functional risk-oriented integrated preventive maintenance considering product quality loss for multistate manufacturing systems[J]. International Journal of Production Research, 2021, 59(4): 1003-1020. doi: 10.1080/00207543.2020.1713416 URL
[16]	WU S M, WANG W B. Optimal inspection policy for three-state systems monitored by control charts[J]. Applied Mathematics and Computation, 2011, 217(23): 9810-9819. doi: 10.1016/j.amc.2011.04.075 URL
[17]	HAN M Y, YANG J H, ZHAO X. Joint optimization of inspection, maintenance, and spare ordering policy considering defective products loss[J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1167-1179. doi: 10.23919/JSEE.2021.000100
[18]	赵斐, 康建设, 王雷震, 等. 考虑非周期检测的视情维修与备件订购联合策略优化[J]. 运筹与管理, 2021, 30(9): 31-37. doi: 10.12005/orms.2021.0276
	ZHAO Fei, KANG Jianshe, WANG Leizhen, et al. Joint policy optimization of condition-based maintenance and spare ordering considering non-periodic inspection[J]. Operations Research and Management Science, 2021, 30(9): 31-37. doi: 10.12005/orms.2021.0276

符号	符号说明	符号	符号说明
P	产品生产率	Γ	时间阈值
C_IC	单位状态检测费用	C_IQ	单位质量检测费用(C_IQ > C_IC)
C_P	单位预防更换费用	C_F	单位故障更换费用
C_D	单位次品损失	C_S	等待备件的单位时间惩罚费用
C_R	备件订购费用	C_H	单位时间库存持有费用

退化系统的质量控制、状态维修与备件订购联合策略优化

Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 18

相关文章 8

编辑推荐

Metrics

本文评价

[1]	叶鸿庆, 苏华德, 郑美妹, 夏唐斌. 考虑双供应商的维护和备件订购联合决策优化[J]. 上海交通大学学报, 2022, 56(10): 1359-1367.
[2]	杨志远, 赵建民, 程中华, 郭驰名, 李俐莹. 多元退化系统维修与备件订购策略优化模型[J]. 上海交通大学学报, 2021, 55(7): 858-867.
[3]	陈永刚, 逄培榕, 杨光, 何鑫. 深水水下管汇工艺管线的质量控制[J]. 海洋工程装备与技术, 2018, 5(增刊): 136-139.
[4]	宋婷婷1,赵子任1，杜世昌1,任斐2，梁鑫光2. 基于马尔科夫模型的关联多工序装配系统装配质量分析[J]. 上海交通大学学报（自然版）, 2018, 52(3): 324-331.
[5]	王威, 鲁瑜, 张宗超, 郭庆, 县申平, 张庆晓. 海管立管腐蚀缺陷修复关键技术及质量控制[J]. 海洋工程装备与技术, 2017, 4(3): 131-135.
[6]	徐锐1，杜世昌1，任斐2，梁鑫光2，刘维红2. 基于马尔科夫模型的关联多工序系统加工质量分析[J]. 上海交通大学学报（自然版）, 2015, 49(05): 669-674.
[7]	许川，吴昊，王华，高翔. 基于高斯映射的多误差源复杂结构匹配分析与优化[J]. 上海交通大学学报（自然版）, 2013, 47(05): 840-845.
[8]	董明，吴一帆，吴畅，侯文皓. 随机需求下半导体生产车间的物流系统仿真优化[J]. 上海交通大学学报（自然版）, 2010, 44(03): 306-0310.