Learning generalizable models for vehicle routing problems via knowledge distillation

• Main Authors: BI, Jieyi, MA, Yining, WANG, Jiahai, CAO, Zhiguang, CHEN, Jinbiao, SUN, Yuan, CHEE, Yeow Meng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Learning systems; Vehicle routing; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8164; https://ink.library.smu.edu.sg/context/sis_research/article/9167/viewcontent/NeurIPS_2022_learning_generalizable_models_for_vehicle_routing_problems_via_knowledge_distillation_Paper_Conference.pdf
• Institution: Singapore Management University

Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results show that, compared with the baseline neural methods, our AMDKD is able to achieve competitive results on both unseen in-distribution and out-of-distribution instances, which are either randomly synthesized or adopted from benchmark datasets (i.e., TSPLIB and CVRPLIB). Notably, our AMDKD is generic, and consumes less computational resources for inference.