ORThought: Benchmarking and Automating Logistics Optimization Modeling

arXiv:2508.14410v3 Announce Type: replace Abstract: Optimization modeling stands as the engine of scientific decision-making in logistics and transportation, yet its adoption is hindered by a steep expertise threshold and the latency of manual workflows. Automating this process via Large Language Models (LLMs) offers a potential solution, but current approaches face critical bottlenecks: (i) a lack of high-quality, complex benchmarks; (ii) methodological inefficiencies in autonomous multi-agent