Investigating the Potential and Performance of Generative AI for a Vehicle Routing Problem

Background: Vehicle routing optimization traditionally requires specialized software and technical expertise, limiting accessibility for small-to-medium enterprises. This study investigates whether generative AI (Claude 3.5 Sonnet via Claude.ai) can provide competitive vehicle routing solutions compared to traditional optimization methods while eliminating technical barriers. Methods: Fifty independent optimization trials were conducted across four methods—Claude.ai (generative AI), VRP Spreadsheet (Linear Programming), Routific (commercial heuristic), and genetic algorithm (evolutionary metaheuristic)—applied to a real-world case study of AED maintenance routing across 80 service locations in Chiang Rai, Thailand. Performance was evaluated across solution quality, ease of use, setup time, and implementation constraints. Results: The Genetic Algorithm achieved the best performance (908.34 km, −27.9% vs. manual routing), followed by Claude.ai best trial (941.64 km, −25.3%), VRP Spreadsheet (949.26 km, −24.7%), and Routific (964.36 km, −23.5%). Notably, Claude.ai’s best trial outperformed deterministic VRP Spreadsheet while requiring only 12 min setup versus 15 min. Probabilistic methods (Claude.ai, Genetic Algorithm) exhibited acceptable variability (CV: 2.24–2.28%), which was substantially lower than typical operational uncertainties. Conclusions: Generative AI provides accessible, competitive vehicle routing optimization, achieving 25%+ improvements with minimal technical expertise, democratizing advanced logistics planning for resource-constrained organizations.