基于性能合同的多部件系统维修策略模型优化

doi:10.11772/j.issn.1001-9081.2020071033

计算机应用 ›› 2021, Vol. 41 ›› Issue (4): 1184-1191.DOI: 10.11772/j.issn.1001-9081.2020071033

所属专题：前沿与综合应用

基于性能合同的多部件系统维修策略模型优化

许飞雪, 刘勤明, 欧阳海玲, 叶春明

上海理工大学管理学院, 上海 200093

收稿日期:2020-07-15 修回日期:2020-09-17 出版日期:2021-04-10 发布日期:2020-10-10
通讯作者: 刘勤明
作者简介:许飞雪（1995—），女，河南商丘人，硕士研究生，主要研究方向：设备维护与决策；刘勤明（1984—），男，山东日照人，副教授，博士，主要研究方向：维护调度、人工智能；欧阳海玲（1998—），女，安徽安庆人，主要研究方向：维护管理；叶春明（1964—），男，安徽宣城人，教授，博士生导师，主要研究方向：生产调度。
基金资助:
国家自然科学基金资助项目（71632008）；上海市自然科学基金资助项目（19ZR1435600）；教育部人文社会科学研究规划基金资助项目（20YJAZH068）；上海理工大学科技发展项目（2020KJFZ038）。

Optimization of maintenance strategy model for multi-component system based on performance-based contract

XU Feixue, LIU Qinming, OUYANG Hailing, YE Chunming

Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2020-07-15 Revised:2020-09-17 Online:2021-04-10 Published:2020-10-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71632008), the Natural Science Foundation of Shanghai (19ZR1435600), the Foundation of Humanities and Social Sciences Research Project of Ministry of Education (20YJAZH068), the Science and Technology Development Project of Shanghai University of Technology (2020KJFZ038).

摘要/Abstract

摘要： 针对多部件串联系统维修效率低下与供应商所获利润少的问题，考虑各部件间的经济相关性，提出基于性能合同的多部件系统的维修策略模型。首先，采用韦伯（Weibull）分布描述系统各部件的使用寿命规律，并通过判断各部件使用度与预防性维修阈值和机会维修阈值的关系来实施不同的维修策略；其次，计算单位更新周期内的各维修活动概率和对应维修次数，以供应商利润最大化为目标，以预防性维修阈值和机会维修阈值为决策变量，建立基于性能合同的多部件系统的维修策略模型；最后，利用灰狼优化（GWO）算法求解该模型。通过算例分析得出与遗传（GA）算法、粒子群优化（PSO）算法相比，灰狼优化算法在准确率上分别提高了22.6%和7.6%；基于线性收益函数，所提绩效模型的利润率高达25.3%，相较于传统成本模型提高了5.2%。基于性能合同的多部件系统维修策略优化模型和算法可以有效解决供应商维修质量与效率低下问题，为供应商和运营商共同制定维修合同提供依据。

关键词: 性能合同, 系统可用度, 机会维修, 多部件系统, 灰狼优化算法

Abstract: Aiming at the problems of the low maintenance efficiency of multi-component series system and the low profit of suppliers, considering the economic correlation between components, a maintenance strategy model for multi-component system based on performance-based contract was proposed. First, Weibull distribution was used to describe the service life law of each component in the system, and different maintenance strategies were implemented by judging the relationship between the usage of each component, the preventive maintenance threshold and the opportunistic maintenance threshold. Second, the probability of each maintenance activity and the corresponding number of maintenances were calculated in the unit renewal cycle, and a maintenance strategy model for multi-component system based on performance-based contract was established with the preventive maintenance threshold and the opportunity maintenance threshold as decision variables and with the goal of maximizing the profit of suppliers. Finally, Grey Wolf Optimizer(GWO) algorithm was used to solve the proposed model. The numerical example analysis showed that compared with Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) algorithm, the improved GWO algorithm had the precision improved by 22.6% and 7.6% respectively, and the profit margin of the proposed performance model was as high as 25.3% based on the linear return function, which was increased by 5.2% compared with that of the traditional cost model. The model and algorithm of multi-component system maintenance strategy optimization based on performance-based contract can effectively solve the problem of low maintenance quality and efficiency of suppliers, and provide a basis for suppliers and operators to jointly make maintenance contracts.

Key words: performance-based contract, system availability, opportunistic maintenance, multi-component system, Grey Wolf Optimizer (GWO) algorithm

中图分类号:

TP20

许飞雪, 刘勤明, 欧阳海玲, 叶春明. 基于性能合同的多部件系统维修策略模型优化[J]. 计算机应用, 2021, 41(4): 1184-1191.

XU Feixue, LIU Qinming, OUYANG Hailing, YE Chunming. Optimization of maintenance strategy model for multi-component system based on performance-based contract[J]. Journal of Computer Applications, 2021, 41(4): 1184-1191.

参考文献

[1] KIM S H,COHEN M A,NETESSINE S. Performance contracting in after-sales service supply chains[J]. Management Science, 2007,53(12):1843-1858.
[2] KIM S H,COHEN M A,NETESSINE S,et al. Contracting for infrequent restoration and recovery of mission-critical systems[J]. Management Science,2010,56(9):1551-1567.
[3] GUAJARDO J A,COHEN M A,KIM S H,et al. Impact of performance-based contracting on product reliability:an empirical analysis[J]. Management Science,2012,58(5):961-979.
[4] PATRA P, KUMAR U D, NOWICKI D R, et al. Effective management of performance-based contracts for sustainment dominant systems[J]. International Journal of Production Economics,2019,208:369-382.
[5] QIU Q, CUI L, SHEN J, et al. Optimal maintenance policy considering maintenance errors for systems operating under performance-based contracts[J]. Computers and Industrial Engineering,2017,112:147-155.
[6] THOMAS L C. A survey of maintenance and replacement models for maintainability and reliability of multi-item system[J]. Reliability Engineering,1986,16(4):297-309.
[7] 杨元, 黎放, 侯重远, 等. 基于相关性的多部件系统机会成组维修优化[J]. 计算机集成制造系统,2012,18(4):827-832. (YANG Y, LI F, HOU C Y, et al. Opportunistic group maintenance optimization of multi-unit system under dependence[J]. Computer Integrated Manufacturing Systems,2012,18(4):827-832.)
[8] 徐波, 韩学山, 孙宏斌, 等. 一种适用于发电机组的机会维修模型[J]. 中国电机工程学报,2018,38(1):120-129.(XU B,HAN X S, SUN H B, et al. A new opportunistic maintenance optimization model for power generating unit[J]. Proceedings of the CSEE,2018,38(1):120-129.)
[9] TIAN Z, JIN T, WU B, et al. Condition based maintenance optimization for wind power generation systems under continuous monitoring[J]. Renewable Energy,2011,36(5):1502-1509.
[10] IUNG B,DO P,LEVRAT E,et al. Opportunistic maintenance based on multi-dependent components of manufacturing system[J]. CIRP Annals,2016,65(1):401-404.
[11] 管业勤, 蒋祖华, 孙小明, 等. 非计划停机下考虑结构相关性的串并联系统机会维修维修决策研究[J/OL]. 工业工程与管理[2020-05-04]. http://kns.cnki.net/kcms/detail/31.1738.T.20200310.1016.004.html. (GUAN Y Q,JIANG Z H,SUN X M, et al. Research on opportunistic maintenance decision method of series-parallel system at unscheduled shutdown considering structural dependency[J]. Industrial Engineering and Management[2020-05-04]. http://kns.cnki.net/kcms/detail/31.1738.T.20200310.1016.004.html.)
[12] 葛小凯, 胡剑波, 张博锋. 考虑依赖性的多部件系统状态维修优化仿真建模[J]. 航空学报,2013,34(8):1854-1863.(GE X K,HU J B,ZHANG B F. Simulation modeling for condition based maintenance optimism of multi-component systems with dependencies[J]. Acta Aeronautica et Astronautica Sinica,2013, 34(8):1854-1863.)
[13] VU H C, DO P, BARROS A, et al. Dynamic grouping maintenance for complex structure systems with non-negligible replacement time[J]. IFAC Proceedings Volumes,2012,45(31):79-84.
[14] 苏春, 胡照勇, 刘洋. 考虑季节因素的风力机多部件机会维修优化[J]. 中南大学学报,2020,27(2):490-499.(SU C,HU Z Y, LIU Y. Multi-component opportunistic maintenance optimization for wind turbines with consideration of seasonal factor[J]. Journal of Central South University, 2020, 27(2):490-499.)
[15] 李志栋, 张涛. 非完美特性下的多状态系统检测与维修优化[J]. 北京航空航天大学学报,2017,43(5):951-960.(LI Z D, ZHANG T. Optimization of inspection and repair of multi-state system under imperfect characteristics[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5):951-960.)
[16] 宋之杰, 常建美, 侯贵宾, 等. 多部件串联系统机会维修策略优化[J]. 机械设计与制造,2015(4):259-262.(SONG Z J, CHANG J M, HOU G B, et al. Optimized opportunistic maintenance strategy for multi-components series system[J]. Machinery Design and Manufacture,2015(4):259-262.)
[17] GIBBONS R. Incentives between firms (and within)[J]. Management Science,2005,51(16):2-17.
[18] NOWICKI D R,KUMAR U D,STEUDEL H J,et al. Spares provisioning under performance-based logistics contract:profitcentric approach[J]. Journal of Operational Research Society, 2008,59(3):342-352.
[19] 李维刚, 李阳, 赵云涛, 等. 基于改进灰狼算法的粒子滤波算法研究[J]. 系仿真学报,2021,33(1):37-45.(LI W G,LI Y, ZHAO Y T,et al. Research on particle filter algorithm based on improved grey wolf algorithm[J]. Journal of System Simulation, 2021,33(1):37-45.)

基于性能合同的多部件系统维修策略模型优化

Optimization of maintenance strategy model for multi-component system based on performance-based contract

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