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End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location. (arXiv:2210.15220v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv facility graph graph neural networks location networks neural networks prediction set