Learning to solve routing problems via distributionally robust optimization

• Main Authors: YUAN, Jiang, WU, Yaoxin, CAO, Zhiguang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Benchmark datasets; Convolutional neural network; Generalization ability; Generalization performance; Learn+; Module-based; Original model; Robust optimization; Routing problems; Synthesised; Software Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8162; https://ink.library.smu.edu.sg/context/sis_research/article/9165/viewcontent/Learning_to_Solve_Routing_Problems_via_Distributionally_Robust_Optimization.pdf
• Institution: Singapore Management University

Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. We also design a module based on convolutional neural network, which allows the deep model to learn more informative latent pattern among the nodes. We evaluate the proposed approach on two types of wellknown deep models including GCN and POMO. The experimental results on the randomly synthesized instances and the ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our approach could significantly improve the cross-distribution generalization performance over the original models.