基于遗传算法和状态信息的多设备混联系统维护调度优化

工业工程

基于遗传算法和状态信息的多设备混联系统维护调度优化

上海理工大学管理学院,上海 200093

出版日期:2016-02-29 发布日期:2016-04-05
作者简介:刘勤明(1984-)，男，山东省人，讲师，博士，主要研究方向为维护调度、人工智能等.
基金资助:
国家自然科学基金资助项目(71471116)；上海市浦江人才计划资助资助(14PJC077)；沪江基金人文社科资助项目(15HJSKYB11)；上海理工大学博士启动基金资助项目(BSQD201403，BSQD201508)

Maintenance Scheduling Optimization on Multi-component Serialparallel System Based on Genetic Algorithm and Prognostic Information

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

Online:2016-02-29 Published:2016-04-05

摘要/Abstract

摘要：

针对多设备混联系统维护优化的建模复杂性，系统分析了设备间的相互依赖性，建立了混联系统的维护调度模型。首先利用威布尔分布模拟设备的衰退过程；定义小修、大修和更换3种维护方式，以及3种维护方式对设备故障率的影响；考虑故障成本、维护成本、资源成本和停机成本，建立了系统一次维护活动的费用模型。其次，基于每次维护活动的费用模型，建立了系统维护多阶段的总费用率模型。最后，通过算例证明了提出的多设备混联系统维护调度优化模型的有效性

关键词: 维护调度, 多设备混联系统, 故障率, 状态信息, 遗传算法

Abstract:

he multi-component maintenance scheduling model is developed, which includes prognostic information, maintenance cost and action. First, the Weibull distribution is used to describe the degradation process. The minor, imperfect and replacement maintenance actions are defined, and the impact on the failure is described. Failure cost, maintenance cost, resource cost and downtime cost are considered. The cost model of one maintenance activity is developed. Then, based on the cost model of each maintenance activity, the multistage total cost rate model is proposed. Finally, the effectiveness of the proposed model is verified by a series of computational experiments.

Key words: maintenance scheduling, multi-component serialparallel system, failure rate, prognostic information, genetic algorithm

刘勤明，孙军华，吕文元，李亚琴. 基于遗传算法和状态信息的多设备混联系统维护调度优化[J]. 工业工程.

LIU Qinming1, SUN Junhua, LYU Wenyuan, LI Yaqin. Maintenance Scheduling Optimization on Multi-component Serialparallel System Based on Genetic Algorithm and Prognostic Information[J]. Industrial Engineering Journal .

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