Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX.
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| Title: | Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX. |
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| Authors: | Farrington, Joseph1 (AUTHOR) ucabjmf@ucl.ac.uk, Wong, Wai Keong1,2,3,4 (AUTHOR), Li, Kezhi1 (AUTHOR) ken.li@ucl.ac.uk, Utley, Martin5 (AUTHOR) |
| Source: | Annals of Operations Research. Jun2025, Vol. 349 Issue 3, p1609-1638. 30p. |
| Subjects: | Artificial intelligence, Computational mathematics, Operations research, Graphics processing units, Process capability |
| Abstract: | Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to represent the age profile of stock. The parallel processing capabilities of modern graphics processing units (GPUs) can reduce the wall time required to run value iteration by updating many states simultaneously. The adoption of GPU-accelerated approaches has been limited in operational research relative to other fields like machine learning, in which new software frameworks have made GPU programming widely accessible. We used the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level interface, and relied on this library's function transformations and compiler to efficiently utilize GPU hardware. Our method can extend use of value iteration to settings that were previously considered infeasible or impractical. We demonstrate this on example scenarios from three recent studies which include problems with over 16 million states and additional problem features, such as substitution between products, that increase computational complexity. We compare the performance of the optimal replenishment policies to heuristic policies, fitted using simulation optimization in JAX which allowed the parallel evaluation of multiple candidate policy parameters on thousands of simulated years. The heuristic policies gave a maximum optimality gap of 2.49%. Our general approach may be applicable to a wide range of problems in operational research that would benefit from large-scale parallel computation on consumer-grade GPU hardware. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of Operations Research is the property of Springer Nature 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185965745 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farrington%2C+Joseph%22">Farrington, Joseph</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ucabjmf@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Wong%2C+Wai+Keong%22">Wong, Wai Keong</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Kezhi%22">Li, Kezhi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ken.li@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Utley%2C+Martin%22">Utley, Martin</searchLink><relatesTo>5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>. Jun2025, Vol. 349 Issue 3, p1609-1638. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+mathematics%22">Computational mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Process+capability%22">Process capability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to represent the age profile of stock. The parallel processing capabilities of modern graphics processing units (GPUs) can reduce the wall time required to run value iteration by updating many states simultaneously. The adoption of GPU-accelerated approaches has been limited in operational research relative to other fields like machine learning, in which new software frameworks have made GPU programming widely accessible. We used the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level interface, and relied on this library's function transformations and compiler to efficiently utilize GPU hardware. Our method can extend use of value iteration to settings that were previously considered infeasible or impractical. We demonstrate this on example scenarios from three recent studies which include problems with over 16 million states and additional problem features, such as substitution between products, that increase computational complexity. We compare the performance of the optimal replenishment policies to heuristic policies, fitted using simulation optimization in JAX which allowed the parallel evaluation of multiple candidate policy parameters on thousands of simulated years. The heuristic policies gave a maximum optimality gap of 2.49%. Our general approach may be applicable to a wide range of problems in operational research that would benefit from large-scale parallel computation on consumer-grade GPU hardware. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Operations Research is the property of Springer Nature 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: 10.1007/s10479-025-06551-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1609 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computational mathematics Type: general – SubjectFull: Operations research Type: general – SubjectFull: Graphics processing units Type: general – SubjectFull: Process capability Type: general Titles: – TitleFull: Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farrington, Joseph – PersonEntity: Name: NameFull: Wong, Wai Keong – PersonEntity: Name: NameFull: Li, Kezhi – PersonEntity: Name: NameFull: Utley, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02545330 Numbering: – Type: volume Value: 349 – Type: issue Value: 3 Titles: – TitleFull: Annals of Operations Research Type: main |
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