InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains

• URL: http://arxiv.org/abs/2407.11384v2
• Date: Fri, 31 Jan 2025 04:31:59 GMT
• Title: InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
• Authors: Yinzhu Quan, Zefang Liu,
• Abstract summary: This study introduces a novel approach using large language models (LLMs) to manage multi-agent inventory systems. Our model, InvAgent, enhances resilience and improves efficiency across the supply chain network.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation [0.5497663232622965]
  The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization. Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices.
  arXiv  Detail & Related papers  (2025-01-26T05:41:50Z)
• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents. We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach [28.477463632107558]
  We develop a modular framework called LLaMAC to address hallucination in Large Language Models and coordination in Multi-Agent Systems. LLaMAC implements a value distribution encoding similar to that found in the human brain, utilizing internal and external feedback mechanisms to facilitate collaboration and iterative reasoning among its modules.
  arXiv  Detail & Related papers  (2023-11-23T10:14:58Z)
• MARLIM: Multi-Agent Reinforcement Learning for Inventory Management [1.1470070927586016]
  This paper presents a novel reinforcement learning framework called MARLIM to address the inventory management problem. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.
  arXiv  Detail & Related papers  (2023-08-03T09:31:45Z)
• A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management [16.808873433821464]
  Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. Applying MARL to real-world scenarios is impeded by many challenges such as scaling up, complex agent interactions, and non-stationary dynamics.
  arXiv  Detail & Related papers  (2023-06-13T05:22:30Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks [0.0]
  We show that proposed neural network controllers (NNCs) are able to learn near-optimal policies of commonly used instances within a few minutes of CPU time. Our research opens up new ways of efficiently managing complex, high-dimensional inventory dynamics.
  arXiv  Detail & Related papers  (2022-01-16T19:44:06Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.