Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems

Authors

  • Liang Xin Nanyang Technological University, Singapore
  • Wen Song Shandong University, China
  • Zhiguang Cao National University of Singapore, Singapore
  • Jie Zhang Nanyang Technological University, Singapore

Keywords:

Routing

Abstract

We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show that our method significantly outperforms the state-of-the-art deep learning based models.

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Published

2021-05-18

How to Cite

Xin, L., Song, W., Cao, Z., & Zhang, J. (2021). Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12042-12049. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17430

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling