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arXiv · 2204.11399

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

Yining Ma·Jingwen Li·Zhiguang Cao·Wen Song·Hongliang Guo·Yuejiao Gong·Yeow Meng Chee
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Abstract

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.

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Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, Yeow Meng Chee. 2022-10-07. Efficient Neural Neighborhood Search for Pickup and Delivery Problems. https://doi.org/10.24963/ijcai.2022%2F662

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