{"title":"A Case Study on the Replacement Policy for a Pan System of Sugar Industry","authors":"H. T. Ba, M. Cholette, Lin Ma, G. Kent","doi":"10.1109/IEEM44572.2019.8978530","DOIUrl":null,"url":null,"abstract":"In sugar production, the vacuum pans used for crystallisation constitute one of most important systems and replacement costs are a significant capital expenditure. The maintenance strategy for pans is currently conducted on a time-based approach. This paper proposes a condition-based maintenance (CBM) model for pans, which is applied to a real multi-component pan operating in an Australian sugar mill. The maintenance policy is formulated as a Markov Decision Process (MDP) and solved via approximate dynamic programming. The results show a 10% saving on total maintenance costs compared to the time-based maintenance strategy.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM44572.2019.8978530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In sugar production, the vacuum pans used for crystallisation constitute one of most important systems and replacement costs are a significant capital expenditure. The maintenance strategy for pans is currently conducted on a time-based approach. This paper proposes a condition-based maintenance (CBM) model for pans, which is applied to a real multi-component pan operating in an Australian sugar mill. The maintenance policy is formulated as a Markov Decision Process (MDP) and solved via approximate dynamic programming. The results show a 10% saving on total maintenance costs compared to the time-based maintenance strategy.
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