Reinforcement Learning Based Optimization of Multi Echelon Inventory and Collaborative Decision Making in Supply Chains: An Algorithmic Innovation Study

Xinru Song

Simen Owen Academic Proceedings Series, 2026, vol. 7, 109-118

Abstract: The optimization of multi-echelon inventory systems represents a fundamental challenge in contemporary supply chain management, particularly when attempting to balance operational cost efficiency with stringent service level requirements. Traditional analytical approaches, including base stock policies and conventional heuristic methods, frequently struggle to accurately capture the dynamic interdependencies across multiple network nodes and the inherently coupled nature of inventory and transportation decisions. This study rigorously investigates the application of advanced reinforcement learning techniques to address these persistent limitations by developing a robust, data-driven decision framework for multi-node supply chain coordination. A comprehensive multi-echelon inventory model is constructed, explicitly capturing stochastic demand patterns, lead time variability, and strict transportation capacity constraints across both serial and divergent supply chain structures. The reinforcement learning agent is systematically trained to learn highly adaptive replenishment and routing policies that effectively minimize total system costs while consistently maintaining target service levels. Unlike conventional methodologies that heavily rely on survey-based or human-interactive data collection, this research strategically employs publicly available supply chain benchmarking datasets and established simulation environments for rigorous model training and evaluation. The proposed algorithmic framework significantly contributes to the emerging literature on artificial intelligence-driven supply chain optimization by demonstrating how reinforcement learning can successfully achieve an optimal cost-service balance without requiring centralized, real-time information sharing. Ultimately, findings from this research offer critical insights for the future development of scalable, resilient, and adaptive inventory management systems within increasingly complex global supply chain networks.

Keywords: reinforcement learning; inventory optimization; supply chain; cost optimization; deep learning (search for similar items in EconPapers)
Date: 2026
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