Deep Learning for Data-Driven Districting-and-Routing

TL;DR

This paper introduces a graph neural network-based approach to improve data-driven districting and routing, achieving significant economic gains by more accurately estimating long-term delivery costs compared to traditional approximation methods.

Contribution

It presents a novel supervised learning methodology using graph neural networks for delivery-cost estimation within an optimization framework for districting.

Findings

Graph neural network predicts long-term district costs more accurately.

Optimizing with the neural network yields 10.12% average economic gains.

District compactness alone does not ensure high-quality solutions.

Abstract

Districting-and-routing is a strategic problem aiming to aggregate basic geographical units (e.g., zip codes) into delivery districts. Its goal is to minimize the expected long-term routing cost of performing deliveries in each district separately. Solving this stochastic problem poses critical challenges since repeatedly evaluating routing costs on a set of scenarios while searching for optimal districts takes considerable time. Consequently, solution approaches usually replace the true cost estimation with continuous cost approximation formulas extending Beardwood-Halton-Hammersley and Daganzo's work. These formulas commit errors that can be magnified during the optimization step. To reconcile speed and solution quality, we introduce a supervised learning and optimization methodology leveraging a graph neural network for delivery-cost estimation. This network is trained to imitate known costs generated on a limited subset of training districts. It is used within an iterated local search procedure to produce high-quality districting plans. Our computational experiments, conducted on five metropolitan areas in the United Kingdom, demonstrate that the graph neural network predicts long-term district cost operations more accurately, and that optimizing over this oracle permits large economic gains (10.12% on average) over baseline methods that use continuous approximation formulas or shallow neural networks. Finally, we observe that having compact districts alone does not guarantee high-quality solutions and that other learnable geometrical features of the districts play an essential role.