End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

TL;DR

This paper introduces a graph neural network-based method for efficiently predicting the entire Pareto set in multi-objective facility location problems, reducing computational costs while maintaining solution quality.

Contribution

It presents a novel learning-based approach modeling MO-FLP as a bipartite graph and using GNNs to predict Pareto distributions, enabling fast non-autoregressive sampling.

Findings

Achieves solution quality comparable to evolutionary algorithms.

Significantly reduces computational cost.

Effective on various problem scales.

Abstract

The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that find a set of Pareto solutions efficiently without enormous search cost. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. We develop a learning-based approach to predicting the distribution probability of the entire Pareto set for a given problem. To this end, the MO-FLP is modeled as a bipartite graph optimization problem and two graph neural networks are constructed to learn the implicit graph representation on nodes and edges. The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively. Experimental results on MO-FLP instances of different scales show that the proposed approach achieves a comparable performance to a widely used multi-objective evolutionary algorithm in terms of the solution quality while significantly reducing the computational cost for search.