{"type":"ScholarlyArticle","title":"Prompt Learning for Generalized Vehicle Routing","authors":[{"name":"Fei Liu","slug":"fei-liu","given":"Fei","family":"Liu","affiliation":"City University of Hong Kong"},{"name":"Xi Lin","slug":"xi-lin","given":"Xi","family":"Lin","affiliation":"City University of Hong Kong"},{"name":"Weiduo Liao","slug":"weiduo-liao","given":"Weiduo","family":"Liao","affiliation":"City University of Hong Kong"},{"name":"Zhenkun Wang","slug":"zhenkun-wang","given":"Zhenkun","family":"Wang","affiliation":"Southern University of Science and Technology"},{"name":"Qingfu Zhang","slug":"qingfu-zhang","given":"Qingfu","family":"Zhang","affiliation":"City University of Hong Kong"},{"name":"Xialiang Tong","slug":"xialiang-tong","given":"Xialiang","family":"Tong","affiliation":"Huawei Noah's Ark Lab"},{"name":"Mingxuan Yuan","slug":"mingxuan-yuan","given":"Mingxuan","family":"Yuan","affiliation":"Huawei Noah's Ark Lab"}],"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. Our code implementation is available online at https://github.com/FeiLiu36/PromptVRP.","doi":"10.24963/ijcai.2024/771","url":"https://ijcai.org/proceedings/2024/771","pdf":"https://ijcai.org/proceedings/2024/0771.pdf","markdown":"https://ijcai.org/proceedings/2024/771.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/771","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Search","track":"Main Track","number":771,"pages":{"first":6976,"last":6984},"keywords":["Search: S: Combinatorial search and optimisation","Search: S: Search and machine learning","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2023] Ruibin Bai, Xinan Chen, Zhi-Long Chen, Tianxiang Cui, Shuhui Gong, Wentao He, Xiaoping Jiang, Huan Jin, Jiahuan Jin, Graham Kendall, et al. Analytics and machine learning in vehicle routing research. Interna- tional Journal of Production Research, 61(1):4-30, 2023.","title":"Analytics and machine learning in vehicle routing research","authors":"Bai","year":"2023","venue":"International Journal of Production Research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bello et al., 2016] Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. Neural combi- natorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940, 2016.","title":"Neural combinatorial optimization with reinforcement learning","authors":"Irwan Bello, Hieu Pham, V Quoc, Mohammad Le, Samy Norouzi, Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2021] Yoshua Bengio, Andrea Lodi, and An- toine Prouvost. Machine learning for combinatorial op- timization: a methodological tour d'horizon. European Journal of Operational Research, 290(2):405-421, 2021.","title":"Machine learning for combinatorial optimization: a methodological tour d'horizon","authors":"Yoshua Bengio, Andrea Lodi","year":"2021","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bi et al., 2022] Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee. Learning generalizable models for vehicle rout- ing problems via knowledge distillation. arXiv preprint arXiv:2210.07686, 2022.","title":"Learning generalizable models for vehicle routing problems via knowledge distillation","authors":"Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bogyrbayeva et al., 2024] Aigerim Bogyrbayeva, Merarys- lan Meraliyev, Taukekhan Mustakhov, and Bissenbay Dauletbayev. Machine learning to solve vehicle routing problems: A survey. IEEE Transactions on Intelligent Transportation Systems, 2024.","title":"Aigerim Bogyrbayeva, Meraryslan Meraliyev, Taukekhan Mustakhov, and Bissenbay Dauletbayev. Machine learning to solve vehicle routing problems: A survey","authors":"Bogyrbayeva","year":"2024","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing sys- tems, 33:1877-1901, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen and Tian, 2019] Xinyun Chen and Yuandong Tian. Learning to perform local rewriting for combinatorial op- timization. Advances in Neural Information Processing Systems, 32, 2019.","title":"Learning to perform local rewriting for combinatorial optimization","authors":"Chen, Tian ; ] Xinyun Chen, Yuandong Tian","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Drakulic et al., 2023] Darko Drakulic, Sofia Michel, Flo- rian Mai, Arnaud Sors, and Jean-Marc Andreoli. Bq-nco: Bisimulation quotienting for generalizable neural combi- natorial optimization. arXiv preprint arXiv:2301.03313, 2023.","title":"Bq-nco: Bisimulation quotienting for generalizable neural combinatorial optimization","authors":"Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, Jean-Marc Andreoli","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fu et al., 2021] Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha. Generalize a small pre-trained model to arbitrarily large tsp instances. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 7474-7482, 2021.","title":"Generalize a small pre-trained model to arbitrarily large tsp instances","authors":"Zhang-Hua Fu, Kai-Bin Qiu, Hongyuan Zha","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ge et al., 2023] Chunjiang Ge, Rui Huang, Mixue Xie, Zi- hang Lai, Shiji Song, Shuang Li, and Gao Huang. Do- main adaptation via prompt learning. IEEE Transactions on Neural Networks and Learning Systems, pages 1-11, 2023.","title":"Domain adaptation via prompt learning","authors":"Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, Gao Huang","year":"2023","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Helsgaun, 2017] Keld Helsgaun. An extension of the lin-kernighan-helsgaun tsp solver for constrained travel- ing salesman and vehicle routing problems. Roskilde: Roskilde University, 12, 2017.","title":"Keld Helsgaun. An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems","authors":"Helsgaun","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hottung and Tierney, 2019] André Hottung and Kevin Tier- ney. Neural large neighborhood search for the ca- pacitated vehicle routing problem. arXiv preprint arXiv:1911.09539, 2019.","title":"Neural large neighborhood search for the capacitated vehicle routing problem","authors":"Tierney ; Hottung, André Hottung, Kevin Tierney","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jia et al., 2022] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision, pages 709-727. Springer, 2022.","title":"Visual prompt tuning","authors":"Jia","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jiang et al., 2022] Yuan Jiang, Yaoxin Wu, Zhiguang Cao, and Jie Zhang. Learning to solve routing problems via distributionally robust optimization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 9786-9794, 2022.","title":"Learning to solve routing problems via distributionally robust optimization","authors":"Yuan Jiang, Yaoxin Wu, Zhiguang Cao, Jie Zhang","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jiang et al., 2023] Yuan Jiang, Zhiguang Cao, Yaoxin Wu, and Jie Zhang. Multi-view graph contrastive learning for solving vehicle routing problems. In Uncertainty in Artifi- cial Intelligence, pages 984-994. PMLR, 2023.","title":"Multi-view graph contrastive learning for solving vehicle routing problems","authors":"Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang","year":"2023","venue":"Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Joshi et al., 2022] Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent. Learning the travelling salesperson problem requires rethinking gen- eralization. Constraints, 27(1-2):70-98, 2022.","title":"Learning the travelling salesperson problem requires rethinking generalization","authors":"K Chaitanya, Quentin Joshi, Louis-Martin Cappart, Thomas Rousseau, Laurent","year":"2022","venue":"Constraints","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kool et al., 2018] Wouter Kool, Herke Van Hoof, and Max Welling. Attention, learn to solve routing problems! arXiv preprint arXiv:1803.08475, 2018.","title":"Attention, learn to solve routing problems!","authors":"Wouter Kool, Herke Van Hoof, Max Welling","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kool et al., 2022] Wouter Kool, Herke van Hoof, Joaquim Gromicho, and Max Welling. Deep policy dynamic programming for vehicle routing problems. In Integra- tion of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022, Proceedings, pages 190-213. Springer, 2022.","title":"Deep policy dynamic programming for vehicle routing problems","authors":"Wouter Kool, Joaquim Herke Van Hoof, Max Gromicho, Welling","year":"2022","venue":"Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kwon et al., 2020] Yeong-Dae Kwon, Jinho Choo, By- oungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min. Pomo: Policy optimization with multiple optima for reinforcement learning. Advances in Neural Information Processing Systems, 33:21188-21198, 2020.","title":"Pomo: Policy optimization with multiple optima for reinforcement learning","authors":"Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2022] Bingjie Li, Guohua Wu, Yongming He, Mingfeng Fan, and Witold Pedrycz. An overview and experimental study of learning-based optimization algo- rithms for the vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 9(7):1115-1138, 2022.","title":"An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem","authors":"Li","year":"2022","venue":"IEEE/CAA Journal of Automatica Sinica","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2023] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1-35, 2023.","title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","authors":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig","year":"2023","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Manchanda et al., 2023] Sahil Manchanda, Sofia Michel, Darko Drakulic, and Jean-Marc Andreoli. On the gen- eralization of neural combinatorial optimization heuris- tics. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V, pages 426-442. Springer, 2023.","title":"On the generalization of neural combinatorial optimization heuristics","authors":"Sahil Manchanda, Sofia Michel, Darko Drakulic, Jean-Marc Andreoli","year":"2022","venue":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nazari et al., 2018] Mohammadreza Nazari, Afshin Oroo- jlooy, Lawrence Snyder, and Martin Takác. Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems, 31, 2018.","title":"Reinforcement learning for solving the vehicle routing problem","authors":"Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, Martin Takác","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ouyang et al., 2022] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instruc- tions with human feedback. Advances in Neural Informa- tion Processing Systems, 35:27730-27744, 2022.","title":"Training language models to follow instructions with human feedback","authors":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Pan et al., 2023] Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, and Jiang Bian. H- tsp: Hierarchically solving the large-scale traveling sales- man problem. In AAAI 2023, February 2023.","title":"Htsp: Hierarchically solving the large-scale traveling salesman problem","authors":"Pan","year":"2023","venue":"AAAI 2023","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Queiroga et al., 2021] Eduardo Queiroga, Ruslan Sadykov, Eduardo Uchoa, and Thibaut Vidal. 10,000 optimal cvrp solutions for testing machine learning based heuristics. In AAAI-22 Workshop on Machine Learning for Operations Research (ML4OR), 2021.","title":"10,000 optimal cvrp solutions for testing machine learning based heuristics","authors":"Eduardo Queiroga, Ruslan Sadykov, Eduardo Uchoa, Thibaut Vidal","year":"2021","venue":"AAAI-22 Workshop on Machine Learning for Operations Research (ML4OR)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Raza et al., 2022] Syed Mohib Raza, Mohammad Sajid, and Jagendra Singh. Vehicle routing problem using reinforce- ment learning: Recent advancements. In Advanced Ma- chine Intelligence and Signal Processing, pages 269-280. Springer, 2022.","title":"Vehicle routing problem using reinforcement learning: Recent advancements","authors":"Mohammad Syed Mohib Raza, Jagendra Sajid, Singh","year":"2022","venue":"Advanced Machine Intelligence and Signal Processing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Toth and Vigo, 2014] Paolo Toth and Daniele Vigo. Vehi- cle routing: problems, methods, and applications. SIAM, 2014.","title":"Vehicle routing: problems, methods, and applications","authors":"Paolo Toth, Daniele Vigo","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Uchoa et al., 2017] Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, and Anand Subra- manian. New benchmark instances for the capacitated ve- hicle routing problem. European Journal of Operational Research, 257(3):845-858, 2017.","title":"New benchmark instances for the capacitated vehicle routing problem","authors":"Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, Anand Subramanian","year":"2017","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Vidal et al., 2013] Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, and Christian Prins. A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Computers & operations research, 40(1):475-489, 2013.","title":"A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows","authors":"Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Christian Prins","year":"2013","venue":"Computers & operations research","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Vinyals et al., 2015] Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. Pointer networks. Advances in neural in- formation processing systems, 28, 2015.","title":"Pointer networks","authors":"Oriol Vinyals, Meire Fortunato, Navdeep Jaitly","year":"2015","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2023] Ning Wang, Jiahao Xie, Jihao Wu, Mingbo Jia, and Linlin Li. Controllable image captioning via prompting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 2617-2625, 2023.","title":"Controllable image captioning via prompting","authors":"Ning Wang, Jiahao Xie, Jihao Wu, Mingbo Jia, Linlin Li","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2022] Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, and Chuang Gan. Prompting decision transformer for few-shot pol- icy generalization. In international conference on machine learning, pages 24631-24645. PMLR, 2022.","title":"Prompting decision transformer for few-shot policy generalization","authors":"Xu","year":"2022","venue":"international conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang et al., 2023] Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li. Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15211-15222, 2023.","title":"Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners","authors":"Zhang","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhou et al., 2022] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022.","title":"Learning to prompt for vision-language models","authors":"Zhou","year":"2022","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhou et al., 2023] Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, and Jie Zhang. Towards omni- generalizable neural methods for vehicle routing prob- lems. In the 40th International Conference on Machine Learning (ICML 2023), 2023.","title":"Towards omnigeneralizable neural methods for vehicle routing problems","authors":"Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang","year":"2023","venue":"the 40th International Conference on Machine Learning (ICML 2023)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}