# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
###### Abstract  
The Fourth Industrial Revolution has introduced \"shared manufacturing\" as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. s 2025 15 May 20  
This approach resonates with the production principles advocated by [PERSON] et al. [4], which emphasize the importance of decentralized, modular, and interconnected systems. A prime example of this paradigm is on-demand warehousing (ODW), where businesses can dynamically access third-party storage facilities to meet their fluctuating needs. Platforms such as Flexe and Ware2 Go exemplify this model, enabling companies to convert fixed leasing costs into variable expenses and enhance their logistical agility [9]. Beyond benefiting small and medium-sized enterprises by reducing market entry barriers, ODW fosters collaborative networks that optimize resource utilization across the supply chain [10], supporting findings on the efficiency of collaborative warehousing [5]. Efficient resource allocation in ODW hinges on a classical optimization challenge akin to the secretary problem, where decisions must be made optimally under uncertainty as options arrive sequentially [11]. In this context, warehouse managers must decide whether to accept or reject storage requests without full knowledge of future opportunities, aiming to maximize profitability. While theoretically well explored, applying this concept in real-world settings is complicated by computational demands and unpredictable variables [12]. Recent studies further illuminate these challenges, offering advanced frameworks for online decision-making [12], analyzing platform-based resource matching [9], optimizing storage allocation with sequential approaches [13], and leveraging AI-driven tools to approximate optimal policies [14]. Recent breakthroughs in artificial intelligence, particularly the advent of LLMs, offer a promising avenue for navigating these challenges. LLMs, with their extensive pre-training on diverse corpora, encode rich semantic representations and emergent reasoning abilities, enabling them to interpret both structured inputs (e.g., capacity figures, timestamps, price quotes) and unstructured negotiation language without bespoke feature engineering [15].