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Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method.

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Název: Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method.
Autoři: Kolyaei, Maryam, Zhang, Lele, Blom, Michelle
Zdroj: International Journal of Production Research; Dec2025, Vol. 63 Issue 24, p9571-9592, 22p
Témata: REINFORCEMENT learning, INVENTORY control, STOCK management (Finance), RETAIL industry, PROFIT maximization, ONLINE shopping, MARKOV processes
Abstrakt: 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.)
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  Data: Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method.
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  Data: International Journal of Production Research; Dec2025, Vol. 63 Issue 24, p9571-9592, 22p
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  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
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  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: Abstract
  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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        Value: 10.1080/00207543.2025.2520596
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        Text: English
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      – SubjectFull: INVENTORY control
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      – SubjectFull: STOCK management (Finance)
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      – SubjectFull: RETAIL industry
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      – SubjectFull: ONLINE shopping
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      – SubjectFull: MARKOV processes
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              Text: Dec2025
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