  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.
