Conditional neural heuristic for multiobjective vehicle routing problems

• Main Authors: FAN, Mingfeng, WU, Yaoxin, CAO, Zhiguang, SONG, Wen, SARTORETTI, Guillaume, LIU, Huan, WU, Guohua
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Context modeling; Decoding; Encoder-decoder; Fans; multiobjective optimization; neural heuristic; Neural networks; Pareto optimization; Training; Vehicle routing; vehicle routing problems; Artificial Intelligence and Robotics; Theory and Algorithms; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/8729; https://ink.library.smu.edu.sg/context/sis_research/article/9732/viewcontent/TNNLS2024_MOVRP_av.pdf
• Institution: Singapore Management University

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neu...