# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 4 Experimental Setup
### Experimental Data  
The experimental setup was designed to evaluate the performance of warehouse agents within a decentralized warehousing system under competitive conditions. Four different simulation scenarios were implemented to evaluate the performance of the proposed shared storage system. The first two scenarios were without a capacity pool mechanism, while the second two were with a capacity pool mechanism. The simulation in this paper focuses more on a scenario where agents utilize a capacity pool mechanism to optimize resource allocation and profit generation. The experimental environment consisted of five autonomous warehouse agents, each managing a warehouse with five slots. To evaluate the effectiveness of different decision-making strategies, four of the agents (Agents 1-4) were configured to use a randomized decision-making method that simulated non-strategic behavior. These agents responded to incoming storage offers by accepting, rejecting, or forwarding them based on predefined probabilities. In contrast, the fifth agent (Agent 5) used an AI-driven decision-making process based on an LLM, allowing it to make context-aware, strategic decisions based on historical and real-time data. This configuration allowed us to compare the performance of a single intelligent agent with that of multiple non-strategic agents in a competitive, decentralized environment. In Scenarios 3 and 4, a capacity pool mechanism was introduced that allowed agents to forward unaccepted offers to the shared pool where other agents could claim them for a fee--an additional layer of complexity and collaboration in the simulation. Table 1 summarizes the key parameters of the experiment. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scenario** & **1** & **2** & **3** & **4** \\ \hline Capacity pool mechanism & Disabled & Disabled & Enabled, allowing offer forwarding & Enabled, allowing offer forwarding \\ \hline Number of simulations per experiment & 5 & 5 & 5 & 5 \\ \hline Warehouse capacity & 1 storage slot per agent & 5 storage slots per agent & 1 storage slot per agent & 5 storage slots per agent \\ \hline Number of warehouse agents & 5 agents (4 random, 1 AI-driven) & 5 agents (4 random, 1 AI-driven) & 5 agents (4 random, 1 AI-driven) & 5 agents (4 random, 1 AI-driven) & 5 agents (4 random, 1 AI-driven) \\ \hline Decision strategy & Random for 4 agents, AI-driven for 1 agent & Random for 4 agents, AI-driven for 1 agent & Random for 4 agents, AI-driven for 1 agent & Random for 4 agents, AI-driven for 1 agent \\ \hline Number of consumers & 5 & 25 & 5 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scenario comparison. The duration of each simulation was set to 100,000 time units, providing ample time for agents' strategies to evolve and their decisions to impact outcomes. Offers for storage services were generated with randomly assigned values between 1 and 10 tokens, and each storage service had a fixed duration of 1000 time units. These parameters created a dynamic and competitive environment for evaluating decision-making methods. Agents with random decision strategy: Simulates non-strategic behavior using fixed probabilities to handle direct offers. Agents with a random decision strategy simulate non-strategic behavior by using fixed probabilities to consider direct offers. In simulations without a capacity pool mechanism, the probability to accept or to reject is 0.5. By using the capacity pool mechanism, in addition to acceptance or rejection, it is also possible to forward the offer. The outcomes and their probabilities are shown in Table 2.  
LLM-driven warehouse agent: Implemented using OpenAI's ChatGPT (GPT-4 version). The experiments were conducted in March 2024. An agent was provided with a structured prompt containing contextual and historical data to make informed decisions.