Modeling of warranty decisions based on optimal preventive replacement considering the structural correlation of multi-component parallel systems Article Swipe
Xuefeng Gao
,
Liang Wen
,
Enzhi Dong
,
Tielu Gao
,
K. Jiang
,
Bo Zhu
,
Yu Wang
,
Zhonghua Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1063/5.0300540
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1063/5.0300540
In today’s highly competitive market, after-sales warranty services have evolved into a strategic differentiator for manufacturers. Within warranty management, manufacturers aim to minimize warranty cost, while users seek maximal system availability. Traditional warranty designs often have limitations, such as focusing only on the usage time of the system and ignoring the structural correlation between system components. This paper establishes a warranty cost model and a warranty availability model for the system, considering structural correlation, and forms a warranty cost-availability ratio model based on these. The model takes the preventive replacement interval of the system as the decision variable, with the two-dimensional warranty period including both the usage time and usage intensity of the system. It aims to minimize the system’s warranty cost-availability ratio, balancing the interest of manufacturers and users. This study takes the air filtration system of a new energy vehicle as an example and uses genetic algorithm to solve the optimal warranty scheme when there is structural correlation between system components. Finally, a sensitivity analysis of the model is conducted to provide practical recommendations for manufacturers’ warranty decision-making.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0300540
- https://pubs.aip.org/aip/adv/article-pdf/doi/10.1063/5.0300540/20738653/105010_1_5.0300540.pdf
- OA Status
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- 31
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https://doi.org/10.1063/5.0300540Digital Object Identifier
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Modeling of warranty decisions based on optimal preventive replacement considering the structural correlation of multi-component parallel systemsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-01Full publication date if available
- Authors
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Xuefeng Gao, Liang Wen, Enzhi Dong, Tielu Gao, K. Jiang, Bo Zhu, Yu Wang, Zhonghua ChengList of authors in order
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https://doi.org/10.1063/5.0300540Publisher landing page
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https://pubs.aip.org/aip/adv/article-pdf/doi/10.1063/5.0300540/20738653/105010_1_5.0300540.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://pubs.aip.org/aip/adv/article-pdf/doi/10.1063/5.0300540/20738653/105010_1_5.0300540.pdfDirect OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.there | 156 |
| abstract_inverted_index.usage | 43, 106, 109 |
| abstract_inverted_index.users | 26 |
| abstract_inverted_index.while | 25 |
| abstract_inverted_index.Within | 16 |
| abstract_inverted_index.energy | 140 |
| abstract_inverted_index.highly | 2 |
| abstract_inverted_index.period | 102 |
| abstract_inverted_index.ratio, | 122 |
| abstract_inverted_index.scheme | 154 |
| abstract_inverted_index.system | 29, 47, 54, 93, 136, 161 |
| abstract_inverted_index.these. | 83 |
| abstract_inverted_index.users. | 129 |
| abstract_inverted_index.between | 53, 160 |
| abstract_inverted_index.designs | 33 |
| abstract_inverted_index.evolved | 9 |
| abstract_inverted_index.example | 144 |
| abstract_inverted_index.genetic | 147 |
| abstract_inverted_index.market, | 4 |
| abstract_inverted_index.maximal | 28 |
| abstract_inverted_index.optimal | 152 |
| abstract_inverted_index.provide | 173 |
| abstract_inverted_index.system, | 70 |
| abstract_inverted_index.system. | 113 |
| abstract_inverted_index.vehicle | 141 |
| abstract_inverted_index.Finally, | 163 |
| abstract_inverted_index.analysis | 166 |
| abstract_inverted_index.decision | 96 |
| abstract_inverted_index.focusing | 39 |
| abstract_inverted_index.ignoring | 49 |
| abstract_inverted_index.interest | 125 |
| abstract_inverted_index.interval | 90 |
| abstract_inverted_index.minimize | 22, 117 |
| abstract_inverted_index.services | 7 |
| abstract_inverted_index.warranty | 6, 17, 23, 32, 60, 65, 77, 101, 120, 153, 178 |
| abstract_inverted_index.algorithm | 148 |
| abstract_inverted_index.balancing | 123 |
| abstract_inverted_index.conducted | 171 |
| abstract_inverted_index.including | 103 |
| abstract_inverted_index.intensity | 110 |
| abstract_inverted_index.practical | 174 |
| abstract_inverted_index.strategic | 12 |
| abstract_inverted_index.today’s | 1 |
| abstract_inverted_index.variable, | 97 |
| abstract_inverted_index.filtration | 135 |
| abstract_inverted_index.preventive | 88 |
| abstract_inverted_index.structural | 51, 72, 158 |
| abstract_inverted_index.system’s | 119 |
| abstract_inverted_index.Traditional | 31 |
| abstract_inverted_index.after-sales | 5 |
| abstract_inverted_index.competitive | 3 |
| abstract_inverted_index.components. | 55, 162 |
| abstract_inverted_index.considering | 71 |
| abstract_inverted_index.correlation | 52, 159 |
| abstract_inverted_index.establishes | 58 |
| abstract_inverted_index.management, | 18 |
| abstract_inverted_index.replacement | 89 |
| abstract_inverted_index.sensitivity | 165 |
| abstract_inverted_index.availability | 66 |
| abstract_inverted_index.correlation, | 73 |
| abstract_inverted_index.limitations, | 36 |
| abstract_inverted_index.availability. | 30 |
| abstract_inverted_index.manufacturers | 19, 127 |
| abstract_inverted_index.differentiator | 13 |
| abstract_inverted_index.manufacturers. | 15 |
| abstract_inverted_index.recommendations | 175 |
| abstract_inverted_index.two-dimensional | 100 |
| abstract_inverted_index.decision-making. | 179 |
| abstract_inverted_index.manufacturers’ | 177 |
| abstract_inverted_index.cost-availability | 78, 121 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.58876729 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |