An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems

arXiv:2511.02525v2 Announce Type: replace Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making location and routing decisions. In CLRPs, the complex constraints and the intricate relationships between various decisions make the problem challenging to solve. With the emergence of deep reinforcement learning (DRL), it has been extensively applied to address the vehicle routing problem and its variants, while the research related to CLRPs still needs to be explored. In this paper, we propose the DRL w

Source: arXiv cs.LG — read the full report at the original publisher.

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