Academic Journal
Online Allocation of Reusable Resources in Nonstationary Environments.
| Τίτλος: | Online Allocation of Reusable Resources in Nonstationary Environments. |
|---|---|
| Συγγραφείς: | Zhang, Xilin1 (AUTHOR) zhangxilin@u.nus.edu, Cheung, Wang Chi1 (AUTHOR) isecwc@nus.edu.sg |
| Πηγή: | Mathematics of Operations Research (INFORMS). Aug2026, Vol. 51 Issue 3, p1853-1883. 31p. |
| Θεματικοί όροι: | *Resource allocation, Epistemic uncertainty, Change-point problems |
| Περίληψη: | We study a general reusable resource allocation model under both model uncertainty and nonstationarity. Our study involves a set of heterogeneous customers who arrive sequentially at the decision maker's (DM's) platform, each associated with a different customer type. Each arriving customer's type is drawn from an unknown and time-varying probability distribution. Upon observing the customer's type, the DM selects an allocation decision that generates a random amount of reward and occupies random amounts of resource units. Each resource unit is occupied for a bounded random duration before becoming available for future allocations. The DM aims to maximize the total reward while ensuring that capacity constraints are met with certainty. Our model captures a variety of applications, such as admission control and assortment planning, in changing environments. We develop dual learning with nonstationarity tests, a multiphase online algorithm that converges to the optimal reward as the resource inventories and horizon length increase under the mild assumption that the inventory amount for each resource is at least logarithmic in the length of the horizon. The algorithm incorporates a dual-learning process for decision making and employs a set of judiciously designed tests to detect potential drifts in the latent nonstationary environment. Funding: This work was supported by the Ministry of Education—Singapore [Grant MOE-T2EP20121-0012]. We acknowledge the funding support from the Singapore Ministry of Education Academic Research Fund Tier 2 [Grant T2EP20121-0035]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0250. [ABSTRACT FROM AUTHOR] |
| Copyright of Mathematics of Operations Research (INFORMS) 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.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Online Allocation of Reusable Resources in Nonstationary Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xilin%22">Zhang, Xilin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangxilin@u.nus.edu</i><br /><searchLink fieldCode="AR" term="%22Cheung%2C+Wang+Chi%22">Cheung, Wang Chi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> isecwc@nus.edu.sg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematics+of+Operations+Research+%28INFORMS%29%22">Mathematics of Operations Research (INFORMS)</searchLink>. Aug2026, Vol. 51 Issue 3, p1853-1883. 31p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Epistemic+uncertainty%22">Epistemic uncertainty</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We study a general reusable resource allocation model under both model uncertainty and nonstationarity. Our study involves a set of heterogeneous customers who arrive sequentially at the decision maker's (DM's) platform, each associated with a different customer type. Each arriving customer's type is drawn from an unknown and time-varying probability distribution. Upon observing the customer's type, the DM selects an allocation decision that generates a random amount of reward and occupies random amounts of resource units. Each resource unit is occupied for a bounded random duration before becoming available for future allocations. The DM aims to maximize the total reward while ensuring that capacity constraints are met with certainty. Our model captures a variety of applications, such as admission control and assortment planning, in changing environments. We develop dual learning with nonstationarity tests, a multiphase online algorithm that converges to the optimal reward as the resource inventories and horizon length increase under the mild assumption that the inventory amount for each resource is at least logarithmic in the length of the horizon. The algorithm incorporates a dual-learning process for decision making and employs a set of judiciously designed tests to detect potential drifts in the latent nonstationary environment. Funding: This work was supported by the Ministry of Education—Singapore [Grant MOE-T2EP20121-0012]. We acknowledge the funding support from the Singapore Ministry of Education Academic Research Fund Tier 2 [Grant T2EP20121-0035]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0250. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mathematics of Operations Research (INFORMS) 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/moor.2023.0250 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 1853 Subjects: – SubjectFull: Resource allocation Type: general – SubjectFull: Epistemic uncertainty Type: general – SubjectFull: Change-point problems Type: general Titles: – TitleFull: Online Allocation of Reusable Resources in Nonstationary Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Xilin – PersonEntity: Name: NameFull: Cheung, Wang Chi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0364765X Numbering: – Type: volume Value: 51 – Type: issue Value: 3 Titles: – TitleFull: Mathematics of Operations Research (INFORMS) Type: main |
| ResultId | 1 |