Multi-type attention for solving multi-depot vehicle routing problems

In recent years, there has been a growing trend towards using deep reinforcement learning (DRL) to solve the NP-hard vehicle routing problems (VRPs). While much success has been achieved, most of the previous studies solely focused on single-depot VRPs, which became less effective in handling more p...

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Main Authors: LI, Jinqi, DAI, Bing Tian, NIU, Yunyun, XIAO, Jianhua, WU, Yaoxin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9208
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spelling sg-smu-ink.sis_research-102132024-08-13T01:24:03Z Multi-type attention for solving multi-depot vehicle routing problems LI, Jinqi DAI, Bing Tian NIU, Yunyun XIAO, Jianhua WU, Yaoxin In recent years, there has been a growing trend towards using deep reinforcement learning (DRL) to solve the NP-hard vehicle routing problems (VRPs). While much success has been achieved, most of the previous studies solely focused on single-depot VRPs, which became less effective in handling more practical scenarios, such as multi-depot VRPs. Although there are many preprocessing measures, such as natural decomposition, those scenarios are still more challenging to optimize. To resolve this issue, we propose the multi-depot multi-type attention (MD-MTA) to solve the multi-depot VRP (MDVRP) and multi-depot open VRP (MDOVRP), respectively. We design a multi-type attention in the network to combine different types of embeddings and the state of the environment at each step, so as to accurately select the next node to visit and construct the route. We introduce a depot rotation augmentation to enhance solution decoding. Results show that it performs favorably against various representative traditional baselines and DRL-based baselines. 2024-06-21T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/9208 info:doi/10.1109/TITS.2024.3413077 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Vehicle routing Transformers Heuristic algorithms Decoding Decision making Computer architecture Training Deep reinforcement learning learning to optimize multi-depot vehicle routing problem multi-depot open vehicle routing problem attention mechanism transformer model Artificial Intelligence and Robotics Operations Research, Systems Engineering and Industrial Engineering Transportation
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Vehicle routing
Transformers
Heuristic algorithms
Decoding
Decision making
Computer architecture
Training
Deep reinforcement learning
learning to optimize
multi-depot vehicle routing problem
multi-depot open vehicle routing problem
attention mechanism
transformer model
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
Transportation
spellingShingle Vehicle routing
Transformers
Heuristic algorithms
Decoding
Decision making
Computer architecture
Training
Deep reinforcement learning
learning to optimize
multi-depot vehicle routing problem
multi-depot open vehicle routing problem
attention mechanism
transformer model
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
Transportation
LI, Jinqi
DAI, Bing Tian
NIU, Yunyun
XIAO, Jianhua
WU, Yaoxin
Multi-type attention for solving multi-depot vehicle routing problems
description In recent years, there has been a growing trend towards using deep reinforcement learning (DRL) to solve the NP-hard vehicle routing problems (VRPs). While much success has been achieved, most of the previous studies solely focused on single-depot VRPs, which became less effective in handling more practical scenarios, such as multi-depot VRPs. Although there are many preprocessing measures, such as natural decomposition, those scenarios are still more challenging to optimize. To resolve this issue, we propose the multi-depot multi-type attention (MD-MTA) to solve the multi-depot VRP (MDVRP) and multi-depot open VRP (MDOVRP), respectively. We design a multi-type attention in the network to combine different types of embeddings and the state of the environment at each step, so as to accurately select the next node to visit and construct the route. We introduce a depot rotation augmentation to enhance solution decoding. Results show that it performs favorably against various representative traditional baselines and DRL-based baselines.
format text
author LI, Jinqi
DAI, Bing Tian
NIU, Yunyun
XIAO, Jianhua
WU, Yaoxin
author_facet LI, Jinqi
DAI, Bing Tian
NIU, Yunyun
XIAO, Jianhua
WU, Yaoxin
author_sort LI, Jinqi
title Multi-type attention for solving multi-depot vehicle routing problems
title_short Multi-type attention for solving multi-depot vehicle routing problems
title_full Multi-type attention for solving multi-depot vehicle routing problems
title_fullStr Multi-type attention for solving multi-depot vehicle routing problems
title_full_unstemmed Multi-type attention for solving multi-depot vehicle routing problems
title_sort multi-type attention for solving multi-depot vehicle routing problems
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9208
_version_ 1814047791547154432