Academic Journal
Benchmarking adaptive pricing policies for Mississippi River container-on-barge service.
| Τίτλος: | Benchmarking adaptive pricing policies for Mississippi River container-on-barge service. |
|---|---|
| Συγγραφείς: | Carroll, Alex1 (AUTHOR) alex629@mit.edu |
| Πηγή: | Annals of Operations Research. Oct2026, p1-33. 33p. |
| Θεματικοί όροι: | *Markov processes, *Container ships, *Economic demand, *Time-based pricing, Reinforcement learning, Inland navigation |
| Γεωγραφικοί όροι: | Mississippi River |
| Περίληψη: | Ports increasingly compete on the performance of their hinterland connections, where inland waterway transport (IWT) can provide high-capacity, lower-emission container flows but faces commercial uncertainty driven by volatile demand and shared asset constraints. This paper studies weekly pricing for an anonymized industry-proposed “fast barge” service serving five inland corridors on the Mississippi River system. We formulate a finite-horizon Markov decision process with stochastic, price-sensitive demand, corridor limits, and a shared fleet-hours allocation problem. Shipment manifests calibrate seasonality but contain no transaction prices, so reference tariffs and elasticities are explicit modeling assumptions. We compare transparent tariff, myopic, one-period, and shadow-price policies with proximal policy optimization (PPO), advantage actor–critic (A2C), and soft actor–critic (SAC), using common continuous actions, ten training seeds, and 200 paired evaluation seeds. A loose reference case remains price-boundary driven. A prespecified 300-hour benchmark has an interior unconstrained price and a positive deterministic capacity dual in 48.1% of weeks. In final evaluation, PPO and SAC improve on the validation-selected transparent benchmark by 0.089% (USD 51,800/year) and 0.362% (USD 211,100/year), respectively, with positive hierarchical intervals. Neither meets the prespecified 0.5% primary adoption screen, and the transparent benchmark retains 99.6% of SAC’s mean profit. The economic case for RL is therefore conditional on whether its annualized implementation and governance costs fall below the measured incremental value, rather than supporting an unconditional replacement recommendation. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Business Source Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10479-026-07444-y Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Benchmarking adaptive pricing policies for Mississippi River container-on-barge service. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Carroll%2C+Alex%22">Carroll, Alex</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alex629@mit.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>. Oct2026, p1-33. 33p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Container+ships%22">Container ships</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+demand%22">Economic demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-based+pricing%22">Time-based pricing</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Inland+navigation%22">Inland navigation</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Mississippi+River%22">Mississippi River</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Ports increasingly compete on the performance of their hinterland connections, where inland waterway transport (IWT) can provide high-capacity, lower-emission container flows but faces commercial uncertainty driven by volatile demand and shared asset constraints. This paper studies weekly pricing for an anonymized industry-proposed “fast barge” service serving five inland corridors on the Mississippi River system. We formulate a finite-horizon Markov decision process with stochastic, price-sensitive demand, corridor limits, and a shared fleet-hours allocation problem. Shipment manifests calibrate seasonality but contain no transaction prices, so reference tariffs and elasticities are explicit modeling assumptions. We compare transparent tariff, myopic, one-period, and shadow-price policies with proximal policy optimization (PPO), advantage actor–critic (A2C), and soft actor–critic (SAC), using common continuous actions, ten training seeds, and 200 paired evaluation seeds. A loose reference case remains price-boundary driven. A prespecified 300-hour benchmark has an interior unconstrained price and a positive deterministic capacity dual in 48.1% of weeks. In final evaluation, PPO and SAC improve on the validation-selected transparent benchmark by 0.089% (USD 51,800/year) and 0.362% (USD 211,100/year), respectively, with positive hierarchical intervals. Neither meets the prespecified 0.5% primary adoption screen, and the transparent benchmark retains 99.6% of SAC’s mean profit. The economic case for RL is therefore conditional on whether its annualized implementation and governance costs fall below the measured incremental value, rather than supporting an unconditional replacement recommendation. [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/s10479-026-07444-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 1 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Container ships Type: general – SubjectFull: Economic demand Type: general – SubjectFull: Time-based pricing Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Inland navigation Type: general – SubjectFull: Mississippi River Type: general Titles: – TitleFull: Benchmarking adaptive pricing policies for Mississippi River container-on-barge service. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Carroll, Alex IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02545330 Titles: – TitleFull: Annals of Operations Research Type: main |
| ResultId | 1 |