SOCA: A Conversational Agent for Facility Location Problems

Facility location problems (FLPs) underpin decisions in emergency response, healthcare access, and infrastructure siting, yet applying them requires both optimization modeling skills and substantial spatial data preparation. This paper presents SOCA (Spatial Optimization Conversational Agent), a system that confines a large language model (LLM) to classifying the formulation and extracting scalar parameters through schema-validated function calls and delegates study area resolution, open-data acquisition, distance computation, and exact solution of four classic FLP formulations (P-Median, P-Center, MCLP, and LSCP) to deterministic tools. Every specification is confirmed by the user before solving, and solutions can be reported with substitutability and spatial equity diagnostics. We present SOCA as a technical feasibility study. Worked examples show how requests become staged, user-confirmed specifications; the MILP encodings reproduce the optima of a reference implementation exactly; and the data pipeline returned the targeted input layers across seven study areas, including cities in Africa and South Asia, using primary or fallback sources; retrieval does not establish data quality. A Manhattan fire station case illustrates coverage trade-offs and post-solve diagnostics on road network distances. Measuring interpretation accuracy, comparison against other LLM tool workflows, and a usability study with non-technical users remain future work.