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 neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly incorporate the problem size into the embedding, so that a single trained model could better solve instances of various scales. Besides, we customize the REINFORCE algorithm to train the neural heuristic by leveraging stochastic preferences (SPs), which further enhances the training performance. Extensive experimental results on random and benchmark instances reveal that our CNH could achieve favorable approximation to the whole PF with higher hypervolume (HV) and lower optimality gap (Gap) than those of the existing neural and conventional heuristics. More importantly, a single trained model of our CNH can outperform other neural heuristics that are exclusively trained on each size. In addition, the effectiveness of the key designs is also verified through ablation studies.