Conditional neural heuristic for multiobjective vehicle routing problems
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...
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sg-smu-ink.sis_research-97322024-04-18T07:31:59Z Conditional neural heuristic for multiobjective vehicle routing problems FAN, Mingfeng WU, Yaoxin CAO, Zhiguang SONG, Wen SARTORETTI, Guillaume LIU, Huan WU, Guohua 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. 2024-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8729 info:doi/10.1109/TNNLS.2024.3371706 https://ink.library.smu.edu.sg/context/sis_research/article/9732/viewcontent/TNNLS2024_MOVRP_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University 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 |
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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 |
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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 FAN, Mingfeng WU, Yaoxin CAO, Zhiguang SONG, Wen SARTORETTI, Guillaume LIU, Huan WU, Guohua Conditional neural heuristic for multiobjective vehicle routing problems |
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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. |
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author |
FAN, Mingfeng WU, Yaoxin CAO, Zhiguang SONG, Wen SARTORETTI, Guillaume LIU, Huan WU, Guohua |
author_facet |
FAN, Mingfeng WU, Yaoxin CAO, Zhiguang SONG, Wen SARTORETTI, Guillaume LIU, Huan WU, Guohua |
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FAN, Mingfeng |
title |
Conditional neural heuristic for multiobjective vehicle routing problems |
title_short |
Conditional neural heuristic for multiobjective vehicle routing problems |
title_full |
Conditional neural heuristic for multiobjective vehicle routing problems |
title_fullStr |
Conditional neural heuristic for multiobjective vehicle routing problems |
title_full_unstemmed |
Conditional neural heuristic for multiobjective vehicle routing problems |
title_sort |
conditional neural heuristic for multiobjective vehicle routing problems |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
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 |
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