首页|Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward
Real-World Multi-Echelon Inventory Optimization
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization
计算技术、计算机技术自动化技术经济
Georg Ziegner,Michael Choi,Hung Mac Chan Le,Sahil Sakhuja,Arash Sarmadi.Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization[EB/OL].(2025-03-23)[2025-10-25].https://arxiv.org/abs/2503.18201.点此复制
Multi-echelon inventory optimization (MEIO) is critical for effective supply
chain management, but its inherent complexity can pose significant challenges.
Heuristics are commonly used to address this complexity, yet they often face
limitations in scope and scalability. Recent research has found deep
reinforcement learning (DRL) to be a promising alternative to traditional
heuristics, offering greater versatility by utilizing dynamic decision-making
capabilities. However, since DRL is known to struggle with the curse of
dimensionality, its relevance to complex real-life supply chain scenarios is
still to be determined. This thesis investigates DRL's applicability to MEIO
problems of increasing complexity. A state-of-the-art DRL model was replicated,
enhanced, and tested across 13 supply chain scenarios, combining diverse
network structures and parameters. To address DRL's challenges with
dimensionality, additional models leveraging graph neural networks (GNNs) and
multi-agent reinforcement learning (MARL) were developed, culminating in the
novel iterative multi-agent reinforcement learning (IMARL) approach. IMARL
demonstrated superior scalability, effectiveness, and reliability in optimizing
inventory policies, consistently outperforming benchmarks. These findings
confirm the potential of DRL, particularly IMARL, to address real-world supply
chain challenges and call for additional research to further expand its
applicability.
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