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InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
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InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
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Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory management is crucial in today's volatile and uncertain world. Previous research has demonstrated the superiority of heuristic methods and reinforcement learning applications in inventory management. However, the application of large language models (LLMs) as autonomous agents in multi-agent systems for inventory management remains underexplored. This study introduces a novel approach using LLMs to manage multi-agent inventory systems. Leveraging their zero-shot learning capabilities, our model, InvAgent, enhances resilience and improves efficiency across the supply chain network. Our contributions include utilizing LLMs for zero-shot learning to enable adaptive and informed decision-making without prior training, providing explainability and clarity through chain-of-thought, and demonstrating dynamic adaptability to varying demand scenarios while reducing costs and preventing stockouts. Extensive evaluations across different scenarios highlight the efficiency of our model in SCM.
Forward citations
Cited by 14 Pith papers
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STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle
Frontier LLM agents detect hidden supply-chain stress almost equally well (84–88% of episodes) but vary from skill 0.62 to −0.23, with two of four models acting worse than ignoring symptoms.
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Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
Autonomous AI agents outperform humans in supply chain simulations but exhibit an inherent agent bullwhip effect of amplified decision unreliability, mitigated by GRPO reinforcement learning post-training.
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gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods
gym-invmgmt is a new benchmarking framework that evaluates inventory policies across optimization and learning methods, finding stochastic programming strongest among non-oracle approaches and PPO-Transformer best amo...
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
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EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments
EconWebArena is a new benchmark with 360 curated economic tasks across 82 authoritative websites for evaluating multimodal web agents on navigation, grounding, and data extraction.
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Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs
Helicase proposes an autonomous multi-agent LLM framework for uncertainty-guided supply chain knowledge graph construction evaluated on the new SCQA benchmark of 80 queries.
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Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
Autonomous generative AI agents outperform humans on average in supply-chain simulations but exhibit decision instability termed agent bullwhip, which GRPO-based post-training mitigates.
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve uses LLMs and RL to generate certified inventory policies that outperform classical and deep learning methods on synthetic and real data while providing multi-period performance guarantees.
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Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Heterogeneous LLM agents in supply chain simulations exhibit myopic self-interested behaviors that worsen inefficiencies, but information sharing mitigates these effects.
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Training-Free Multimodal Large Language Model Orchestration
LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
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Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
AI agents in supply chain simulations outperform humans but exhibit decision instability that GRPO post-training reduces.
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Training-Free Multimodal Large Language Model Orchestration
A training-free orchestration framework integrates off-the-shelf modality experts via an LLM controller, text-centric cross-modal memory, and unified interaction layer to enable multimodal input-output without joint training.
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Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
LLM-based agents simulating supply chain tiers exhibit known behavioral biases, and information sharing mitigates resulting inefficiencies.
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