Adaptive Risk-Aware Planning in Multi-Echelon Supply Chains via Distributional Reinforcement Learning

Long Liang

doi:10.54097/egqtdy48

Authors

Long Liang

DOI:

https://doi.org/10.54097/egqtdy48

Keywords:

Distributional reinforcement learning, Multi-echelon supply chain, Risk-aware planning, Inventory optimization, Sequential decision-making, Conditional value-at-risk

Abstract

Multi-echelon supply chain management faces unprecedented challenges from demand uncertainties, disruption risks, and complex interdependencies across network tiers. Traditional optimization approaches struggle to balance risk mitigation with operational efficiency in dynamic environments where distributional properties of returns significantly impact decision quality. This research proposes an adaptive risk-aware planning framework that leverages Distributional Reinforcement Learning (DRL) to optimize inventory positioning and order decisions across multi-echelon supply networks. Unlike conventional reinforcement learning methods that optimize expected returns, the proposed distributional approach explicitly models the full return distribution, enabling risk-sensitive policies that account for tail risks and variance. The framework incorporates a Quantile Regression Deep Q-Network architecture enhanced with attention mechanisms to capture temporal dependencies and inter-echelon coordination requirements. Experimental validation on benchmark multi-echelon supply chain scenarios demonstrates that the distributional approach achieves 18-24% reduction in total supply chain costs compared to traditional base-stock policies while maintaining service level constraints. The risk-aware policies exhibit superior robustness under demand volatility, reducing inventory variance by 31% and backorder occurrences by 42%. Furthermore, the learned policies demonstrate strong generalization capabilities across different demand distributions and network configurations. This research contributes to both theoretical understanding of risk-aware sequential decision-making in supply chains and provides practitioners with computationally tractable methods for adaptive multi-echelon planning under uncertainty.

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