Prompt Learning for Generalized Vehicle Routing

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

View graph of relations

Author(s)

  • Zhenkun Wang
  • Xialiang Tong
  • Mingxuan Yuan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
PublisherInternational Joint Conferences on Artificial Intelligence
Publication statusAccepted/In press/Filed - 17 Apr 2024

Conference

Title33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
Location
PlaceKorea, Republic of
CityJeju
Period3 - 9 August 2024

Abstract

Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the current NCO methods mainly focus on the in-distribution performance, while the real-world problem instances usually come from different distributions. A costly fine-tuning approach or generalized model retraining from scratch could be needed to tackle the out-of-distribution instances. Unlike the existing methods, this work investigates an efficient prompt learning approach in NCO for cross-distribution adaptation. To be concrete, we propose a novel prompt learning method to facilitate fast zero-shot adaptation of a pre-trained model to solve routing problem instances from different distributions. The proposed model learns a set of prompts among various distributions and then selects the best-matched one to prompt a pre-trained attention model for each problem instance. Extensive experiments show that the proposed prompt learning approach facilitates the fast adaptation of pre-trained routing models. It also outperforms existing generalized models on both in-distribution prediction and zero-shot generalization to a diverse set of new tasks.

Bibliographic Note

Since this conference is yet to commence, the information for this record is subject to revision.

Citation Format(s)

Prompt Learning for Generalized Vehicle Routing. / Liu, Fei; Lin, Xi; Liao, Weiduo et al.
Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024). International Joint Conferences on Artificial Intelligence, 2024.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review