Efficient Neural Neighborhood Search for Pickup and Delivery Problems
This addresses routing optimization for logistics and transportation, offering a novel neural method with competitive performance.
The paper tackles pickup and delivery problems by proposing an efficient Neural Neighborhood Search approach, achieving state-of-the-art results among neural methods and outperforming the LKH3 solver on a constrained variant.
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.