Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning

TL;DR

This paper introduces a knowledge-informed reinforcement learning approach for large-scale urban facility location problems, achieving near-optimal solutions with significantly faster inference than traditional methods.

Contribution

The paper presents a novel RL method using graph neural networks and swap operations to efficiently solve large-scale urban FLP without heavy local search computations.

Findings

Achieves less than 5% accessibility loss compared to commercial solvers.

Provides up to 1000 times faster inference speed.

Successfully applied to four US cities with diverse geospatial conditions.

Abstract

The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-scale urban FLP, capable of producing near-optimal solutions at superfast inference speed. We distill the essential swap operation from local search, and simulate it by intelligently selecting edges on a graph of urban regions, guided by a knowledge-informed graph neural network, thus sidestepping the need for heavy computation of local search. Extensive experiments on four US cities with different geospatial conditions demonstrate that our approach can achieve comparable performance to commercial solvers with less than 5\% accessibility loss, while displaying up to 1000 times speedup. We deploy our model as an online geospatial application at https://huggingface.co/spaces/randommmm/MFLP.