April 4, 2024, 4:42 a.m. | Haofeng Yuan, Rongping Zhu, Wanlu Yang, Shiji Song, Keyou You, Yuli Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.02476v1 Announce Type: cross
Abstract: The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning simultaneously, which, however, leads to exact methods with high computational cost and heuristics with sophisticated design but limited performance. In sharp contrast, we propose a novel approach based on deep reinforcement learning (DRL), which addresses route construction and purchase planning separately, while …

abstract applications arxiv computational construction cost cs.ai cs.lg heuristics however leads math.oc optimization planning purchase reinforcement reinforcement learning route routing type

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