Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs

IF 1.8 Q3 ENGINEERING, INDUSTRIAL
Velmurugan Kumaresan, S. Saravanasankar, G. Di Bona
{"title":"Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs","authors":"Velmurugan Kumaresan, S. Saravanasankar, G. Di Bona","doi":"10.1108/jqme-10-2022-0070","DOIUrl":null,"url":null,"abstract":"PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.Design/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.FindingsThe major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quality in Maintenance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jqme-10-2022-0070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

Abstract

PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.Design/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.FindingsThe major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.
为中小企业的最佳维护和服务管理系统确定最佳维护参数
目的通过使用马尔可夫决策模型(MDM)方法,本研究揭示了中小型企业(SMEs)在故障和理想情况下机器可用性的显著变化。为了获得最低的投资成本,本研究在中小企业的维护操作中采用了一种非传统的优化策略。它将使用粒子群优化(PSO)算法来优化机器维护参数并找到最佳解决方案,从而为最佳维护和服务操作引入最佳决策过程。研究结果本研究的主要目标是识别制造工厂中的关键子系统,并使用最佳决策过程来采用行业中的最佳维护管理系统。原创性/价值根据上述研究结果,为中小企业的预防性维护管理提出了新的最佳决策支持系统,其目的是通过最佳计划和调度流程,以最少的维护和服务支出实现最高的生产率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Quality in Maintenance Engineering
Journal of Quality in Maintenance Engineering Engineering-Safety, Risk, Reliability and Quality
CiteScore
4.00
自引率
13.30%
发文量
24
期刊介绍: This exciting journal looks at maintenance engineering from a positive standpoint, and clarifies its recently elevatedstatus as a highly technical, scientific, and complex field. Typical areas examined include: ■Budget and control ■Equipment management ■Maintenance information systems ■Process capability and maintenance ■Process monitoring techniques ■Reliability-based maintenance ■Replacement and life cycle costs ■TQM and maintenance
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信