Joint optimization of ordering and maintenance with condition monitoring data
Abstract We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the init...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131881 |
| _version_ | 1811084500121157632 |
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| author | Moghaddass, Ramin Ertekin, Şeyda |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Moghaddass, Ramin Ertekin, Şeyda |
| author_sort | Moghaddass, Ramin |
| collection | MIT |
| description | Abstract
We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities. We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a partially observable semi-Markov decision process and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show thatour joint policy outperforms conventional suboptimal policies commonly used in theliterature. |
| first_indexed | 2024-09-23T12:51:45Z |
| format | Article |
| id | mit-1721.1/131881 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:51:45Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1318812023-12-12T15:44:15Z Joint optimization of ordering and maintenance with condition monitoring data Moghaddass, Ramin Ertekin, Şeyda Sloan School of Management Abstract We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities. We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a partially observable semi-Markov decision process and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show thatour joint policy outperforms conventional suboptimal policies commonly used in theliterature. 2021-09-20T17:30:47Z 2021-09-20T17:30:47Z 2018-01-04 2020-09-24T21:39:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131881 en https://doi.org/10.1007/s10479-017-2745-3 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Moghaddass, Ramin Ertekin, Şeyda Joint optimization of ordering and maintenance with condition monitoring data |
| title | Joint optimization of ordering and maintenance with condition monitoring data |
| title_full | Joint optimization of ordering and maintenance with condition monitoring data |
| title_fullStr | Joint optimization of ordering and maintenance with condition monitoring data |
| title_full_unstemmed | Joint optimization of ordering and maintenance with condition monitoring data |
| title_short | Joint optimization of ordering and maintenance with condition monitoring data |
| title_sort | joint optimization of ordering and maintenance with condition monitoring data |
| url | https://hdl.handle.net/1721.1/131881 |
| work_keys_str_mv | AT moghaddassramin jointoptimizationoforderingandmaintenancewithconditionmonitoringdata AT ertekinseyda jointoptimizationoforderingandmaintenancewithconditionmonitoringdata |