From Words to Routes: Applying Large Language Models to Vehicle Routing
arxiv(2024)
摘要
LLMs have shown impressive progress in robotics (e.g., manipulation and
navigation) with natural language task descriptions. The success of LLMs in
these tasks leads us to wonder: What is the ability of LLMs to solve vehicle
routing problems (VRPs) with natural language task descriptions? In this work,
we study this question in three steps. First, we construct a dataset with 21
types of single- or multi-vehicle routing problems. Second, we evaluate the
performance of LLMs across four basic prompt paradigms of text-to-code
generation, each involving different types of text input. We find that the
basic prompt paradigm, which generates code directly from natural language task
descriptions, performs the best for GPT-4, achieving 56
optimality, and 53
not be able to provide correct solutions at the initial attempt, we propose a
framework that enables LLMs to refine solutions through self-reflection,
including self-debugging and self-verification. With GPT-4, our proposed
framework achieves a 16
and a 15
to task descriptions, specifically focusing on how its performance changes when
certain details are omitted from the task descriptions, yet the core meaning is
preserved. Our findings reveal that such omissions lead to a notable decrease
in performance: 4
Website: https://sites.google.com/view/words-to-routes/
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