Adaptive Inventory Strategies using Deep Reinforcement Learning for Dynamic Agri-Food Supply Chains Article Swipe

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Amandeep Kaur , Gyan Prakash · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.16670

Agricultural products are often subject to seasonal fluctuations in production and demand. Predicting and managing inventory levels in response to these variations can be challenging, leading to either excess inventory or stockouts. Additionally, the coordination among stakeholders at various level of food supply chain is not considered in the existing body of literature. To bridge these research gaps, this study focuses on inventory management of agri-food products under demand and lead time uncertainties. By implementing effective inventory replenishment policy results in maximize the overall profit throughout the supply chain. However, the complexity of the problem increases due to these uncertainties and shelf-life of the product, that makes challenging to implement traditional approaches to generate optimal set of solutions. Thus, the current study propose a novel Deep Reinforcement Learning (DRL) algorithm that combines the benefits of both value- and policy-based DRL approaches for inventory optimization under uncertainties. The proposed algorithm can incentivize collaboration among stakeholders by aligning their interests and objectives through shared optimization goal of maximizing profitability along the agri-food supply chain while considering perishability, and uncertainty simultaneously. By selecting optimal order quantities with continuous action space, the proposed algorithm effectively addresses the inventory optimization challenges. To rigorously evaluate this algorithm, the empirical data from fresh agricultural products supply chain inventory is considered. Experimental results corroborate the improved performance of the proposed inventory replenishment policy under stochastic demand patterns and lead time scenarios. The research findings hold managerial implications for policymakers to manage the inventory of agricultural products more effectively under uncertainty.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2507.16670

PDF

https://arxiv.org/pdf/2507.16670

OA Status

green

OpenAlex ID

https://openalex.org/W4417440156

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OpenAlex ID

https://openalex.org/W4417440156

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DOI

https://doi.org/10.48550/arxiv.2507.16670

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Title

Adaptive Inventory Strategies using Deep Reinforcement Learning for Dynamic Agri-Food Supply Chains

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preprint

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Language

en

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Publication year

2025

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Publication date

2025-07-22

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Amandeep Kaur, Gyan Prakash

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https://arxiv.org/abs/2507.16670

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https://arxiv.org/pdf/2507.16670

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Yes

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OA status

green

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https://arxiv.org/pdf/2507.16670

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0

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