Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Software and Cyber-Physical Systems

Publication

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Vienna, Austria, 2022 Jul 23-29

First Page

4776

Last Page

4784

ISBN

9781956792003

Identifier

10.48550/arXiv.2204.11399

Publisher

International Joint Conferences on Artificial Intelligence

City or Country

California

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.48550/arXiv.2204.11399

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