Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method.
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| Title: | Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method. |
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| Authors: | Kolyaei, Maryam1 (AUTHOR) mkolyaei@student.unimelb.edu.au, Zhang, Lele1 (AUTHOR), Blom, Michelle2 (AUTHOR) |
| Source: | International Journal of Production Research. Dec2025, Vol. 63 Issue 24, p9571-9592. 22p. |
| Subjects: | Reinforcement learning, Inventory control, Stock management (Finance), Retail industry, Profit maximization, Online shopping, Markov processes |
| Abstract: | We address the replenishment and fulfilment challenges faced by an omnichannel retailer within a capacitated retail network, selling products to a large region across a multi-period horizon. This horizon is partitioned into cycles, where replenishment occurs at the start of each cycle and fulfilment decisions regarding how much to replenish and allocate across sales channels take place in each time period. Our model considers Click and Collect (C&C) – also known as Buy Online and Pick-up in Store (BOPS) – as well as ship-from-store strategies with the aim of maximising the retailer's expected total profit. The problem is formulated as a Markov Decision Process (MDP). To solve the MDP, a tailored Proximal Policy Optimisation (PPO) algorithm, a form of Deep Reinforcement Learning (DRL), is adopted. We conduct experiments across varying product and store numbers, store capacities, and demand variability to evaluate the performance and robustness of our approach. Furthermore, we evaluate the impact of different demand patterns by first training decision-making policies on specific patterns and then testing them on alternative patterns. Our results reveal that the tailored approach effectively handles high-dimensional decision-making, different demand patterns, uncertainty, and constrained capacity environments while improving profitability compared to baseline methods. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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: 189933513 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kolyaei%2C+Maryam%22">Kolyaei, Maryam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mkolyaei@student.unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lele%22">Zhang, Lele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Blom%2C+Michelle%22">Blom, Michelle</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Dec2025, Vol. 63 Issue 24, p9571-9592. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Inventory+control%22">Inventory control</searchLink><br /><searchLink fieldCode="DE" term="%22Stock+management+%28Finance%29%22">Stock management (Finance)</searchLink><br /><searchLink fieldCode="DE" term="%22Retail+industry%22">Retail industry</searchLink><br /><searchLink fieldCode="DE" term="%22Profit+maximization%22">Profit maximization</searchLink><br /><searchLink fieldCode="DE" term="%22Online+shopping%22">Online shopping</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We address the replenishment and fulfilment challenges faced by an omnichannel retailer within a capacitated retail network, selling products to a large region across a multi-period horizon. This horizon is partitioned into cycles, where replenishment occurs at the start of each cycle and fulfilment decisions regarding how much to replenish and allocate across sales channels take place in each time period. Our model considers Click and Collect (C&C) – also known as Buy Online and Pick-up in Store (BOPS) – as well as ship-from-store strategies with the aim of maximising the retailer's expected total profit. The problem is formulated as a Markov Decision Process (MDP). To solve the MDP, a tailored Proximal Policy Optimisation (PPO) algorithm, a form of Deep Reinforcement Learning (DRL), is adopted. We conduct experiments across varying product and store numbers, store capacities, and demand variability to evaluate the performance and robustness of our approach. Furthermore, we evaluate the impact of different demand patterns by first training decision-making policies on specific patterns and then testing them on alternative patterns. Our results reveal that the tailored approach effectively handles high-dimensional decision-making, different demand patterns, uncertainty, and constrained capacity environments while improving profitability compared to baseline methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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: 10.1080/00207543.2025.2520596 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 9571 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Inventory control Type: general – SubjectFull: Stock management (Finance) Type: general – SubjectFull: Retail industry Type: general – SubjectFull: Profit maximization Type: general – SubjectFull: Online shopping Type: general – SubjectFull: Markov processes Type: general Titles: – TitleFull: Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kolyaei, Maryam – PersonEntity: Name: NameFull: Zhang, Lele – PersonEntity: Name: NameFull: Blom, Michelle IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 24 Titles: – TitleFull: International Journal of Production Research Type: main |
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