Academic Journal
Perspective Benders Decomposition with Applications to Fixed-Charge Nonlinear Resource Allocation.
| Τίτλος: | Perspective Benders Decomposition with Applications to Fixed-Charge Nonlinear Resource Allocation. |
|---|---|
| Συγγραφείς: | Yang, Kang, Song, Guopeng, Wang, Rui, Yang, Haoxiang, Leus, Roel |
| Πηγή: | INFORMS Journal on Computing; May/Jun2026, Vol. 38 Issue 3, p783-801, 19p |
| Θεματικοί όροι: | Sensor placement, Location problems (Programming), Mixed integer linear programming, Mathematical optimization, Nonlinear programming |
| Περίληψη: | Decision-making processes involving fixed charges arise in various real-world applications and can often be modeled as mixed-integer nonlinear programs (MINLPs) with semicontinuous variables. Perspective reformulation, a technique leveraging perspective functions, offers tight formulations for such MINLPs. In this article, we address the challenge of solving such reformulations by introducing perspective Benders cuts, a family of generalized Benders optimality cuts, and compare them with the classic generalized Benders cuts and the perspective cuts. We focus on their applications to two fixed-charge nonlinear resource allocation problems: a generalized sensor placement problem and a generalized uncapacitated facility location problem. The original quadratic allocation cost functions in these problems are extended to a class of reducible convex functions. By leveraging the reducible property of nonlinear resource allocation problems, we develop an ad-hoc procedure of solving the reduced quadratic subproblems to efficiently separate perspective Benders cuts. These features contribute to a highly efficient branch-and-Benders-cut approach, as demonstrated through extensive computational experiments on various sets of benchmark instances. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: K. Yang acknowledges financial support from China Scholarship Council [Grant 202406110031]. This work was also supported by the National Science Fund for Outstanding Young Scholars [Grant 62122093], the National Natural Science Foundation of China [Grants 72101264, 72431011, and 72421002], the Science and Technology Innovation Program of Hunan Province [Grant 2023RC3008], Open Project of Xiangjiang Laboratory [Grant 22XJ02003], and the University Fundamental Research Fund [Grant 23-ZZCX-JDZ-28]. H. Yang's work is funded by National Natural Science Foundation of China [Grant 72201232 and 72231008], Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001], and Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network [Grant ZDSYS20220606100601002]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0984) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0984). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] |
| Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 194555545 RelevancyScore: 1141 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1140.76245117188 |
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| Items | – Name: Title Label: Title Group: Ti Data: Perspective Benders Decomposition with Applications to Fixed-Charge Nonlinear Resource Allocation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Kang%22">Yang, Kang</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Guopeng%22">Song, Guopeng</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Rui%22">Wang, Rui</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Haoxiang%22">Yang, Haoxiang</searchLink><br /><searchLink fieldCode="AR" term="%22Leus%2C+Roel%22">Leus, Roel</searchLink> – Name: TitleSource Label: Source Group: Src Data: INFORMS Journal on Computing; May/Jun2026, Vol. 38 Issue 3, p783-801, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Sensor+placement%22">Sensor placement</searchLink><br /><searchLink fieldCode="DE" term="%22Location+problems+%28Programming%29%22">Location problems (Programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+programming%22">Nonlinear programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Decision-making processes involving fixed charges arise in various real-world applications and can often be modeled as mixed-integer nonlinear programs (MINLPs) with semicontinuous variables. Perspective reformulation, a technique leveraging perspective functions, offers tight formulations for such MINLPs. In this article, we address the challenge of solving such reformulations by introducing perspective Benders cuts, a family of generalized Benders optimality cuts, and compare them with the classic generalized Benders cuts and the perspective cuts. We focus on their applications to two fixed-charge nonlinear resource allocation problems: a generalized sensor placement problem and a generalized uncapacitated facility location problem. The original quadratic allocation cost functions in these problems are extended to a class of reducible convex functions. By leveraging the reducible property of nonlinear resource allocation problems, we develop an ad-hoc procedure of solving the reduced quadratic subproblems to efficiently separate perspective Benders cuts. These features contribute to a highly efficient branch-and-Benders-cut approach, as demonstrated through extensive computational experiments on various sets of benchmark instances. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: K. Yang acknowledges financial support from China Scholarship Council [Grant 202406110031]. This work was also supported by the National Science Fund for Outstanding Young Scholars [Grant 62122093], the National Natural Science Foundation of China [Grants 72101264, 72431011, and 72421002], the Science and Technology Innovation Program of Hunan Province [Grant 2023RC3008], Open Project of Xiangjiang Laboratory [Grant 22XJ02003], and the University Fundamental Research Fund [Grant 23-ZZCX-JDZ-28]. H. Yang's work is funded by National Natural Science Foundation of China [Grant 72201232 and 72231008], Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001], and Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network [Grant ZDSYS20220606100601002]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0984) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0984). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/ijoc.2024.0984 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 783 Subjects: – SubjectFull: Sensor placement Type: general – SubjectFull: Location problems (Programming) Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Nonlinear programming Type: general Titles: – TitleFull: Perspective Benders Decomposition with Applications to Fixed-Charge Nonlinear Resource Allocation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Kang – PersonEntity: Name: NameFull: Song, Guopeng – PersonEntity: Name: NameFull: Wang, Rui – PersonEntity: Name: NameFull: Yang, Haoxiang – PersonEntity: Name: NameFull: Leus, Roel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May/Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10919856 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: INFORMS Journal on Computing Type: main |
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