[2604.04383] Optimizing Service Operations via LLM-Powered Multi-Agent Simulation

Computer Science > Artificial Intelligence arXiv:2604.04383 (cs) [Submitted on 6 Apr 2026] Title:Optimizing Service Operations via LLM-Powered Multi-Agent Simulation Authors:Yanyuan Wang, Xiaowei Zhang View a PDF of the paper titled Optimizing Service Operations via LLM-Powered Multi-Agent Simulation, by Yanyuan Wang and Xiaowei Zhang View PDF HTML (experimental) Abstract:Service system performance depends on how participants respond to design choices, but modeling these responses is hard due to the complexity of human behavior. We introduce an LLM-powered multi-agent simulation (LLM-MAS) framework for optimizing service operations. We pose the problem as stochastic optimization with decision-dependent uncertainty: design choices are embedded in prompts and shape the distribution of outcomes from interacting LLM-powered agents. By embedding key numerical information in prompts and extracting it from LLM-generated text, we model this uncertainty as a controlled Markov chain. We develop an on-trajectory learning algorithm that, on a single simulation run, simultaneously constructs zeroth-order gradient estimates and updates design parameters to optimize steady-state performance. We also incorporate variance reduction techniques. In a sustainable supply chain application, our method outperforms benchmarks, including blackbox optimization and using LLMs as numerical solvers or as role-playing system designers. A case study on optimal contest design with real behavioral data sh...