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Article

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

by
Sakgasem Ramingwong
1,2 and
Jutamat Jintana
1,3,*
1
Supply Chain and Engineering Management Research Unit, Chiang Mai University, Chiang Mai 50200, Thailand
2
Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
3
Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai 50200, Thailand
*
Author to whom correspondence should be addressed.
Logistics 2026, 10(6), 120; https://doi.org/10.3390/logistics10060120
Submission received: 11 April 2026 / Revised: 12 May 2026 / Accepted: 18 May 2026 / Published: 1 June 2026
(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)

Abstract

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.
Keywords: vehicle routing problem; generative AI; large language models; Claude.ai; genetic algorithm; route optimization; logistics management; artificial intelligence vehicle routing problem; generative AI; large language models; Claude.ai; genetic algorithm; route optimization; logistics management; artificial intelligence

Share and Cite

MDPI and ACS Style

Ramingwong, S.; Jintana, J. Investigating the Potential and Performance of Generative AI for a Vehicle Routing Problem. Logistics 2026, 10, 120. https://doi.org/10.3390/logistics10060120

AMA Style

Ramingwong S, Jintana J. Investigating the Potential and Performance of Generative AI for a Vehicle Routing Problem. Logistics. 2026; 10(6):120. https://doi.org/10.3390/logistics10060120

Chicago/Turabian Style

Ramingwong, Sakgasem, and Jutamat Jintana. 2026. "Investigating the Potential and Performance of Generative AI for a Vehicle Routing Problem" Logistics 10, no. 6: 120. https://doi.org/10.3390/logistics10060120

APA Style

Ramingwong, S., & Jintana, J. (2026). Investigating the Potential and Performance of Generative AI for a Vehicle Routing Problem. Logistics, 10(6), 120. https://doi.org/10.3390/logistics10060120

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