Learning to iteratively solve routing problems with dual-aspect collaborative transformer

• Main Authors: MA, Yining, LI, Jingwen, CAO, Zhiguang, SONG, Wen, ZHANG, Le, CHEN, Zhenghua, TANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Benchmarking; Encoding (symbols); Iterative methods; Learning systems; Signal encoding; Traveling salesman problem; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8161; https://ink.library.smu.edu.sg/context/sis_research/article/9164/viewcontent/NeurIPS_2021_learning_to_iteratively_solve_routing_problems_with_dual_aspect_collaborative_transformer_Paper.pdf
• Institution: Singapore Management University

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dua...