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Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning
In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck’s capacity restrictions and adh...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135236/ https://www.ncbi.nlm.nih.gov/pubmed/35617306 http://dx.doi.org/10.1371/journal.pone.0267199 |
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author | Alharbi, Majed G. Stohy, Ahmed Elhenawy, Mohammed Masoud, Mahmoud Khalifa, Hamiden Abd El-Wahed |
author_facet | Alharbi, Majed G. Stohy, Ahmed Elhenawy, Mohammed Masoud, Mahmoud Khalifa, Hamiden Abd El-Wahed |
author_sort | Alharbi, Majed G. |
collection | PubMed |
description | In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck’s capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning. |
format | Online Article Text |
id | pubmed-9135236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91352362022-05-27 Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning Alharbi, Majed G. Stohy, Ahmed Elhenawy, Mohammed Masoud, Mahmoud Khalifa, Hamiden Abd El-Wahed PLoS One Research Article In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck’s capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning. Public Library of Science 2022-05-26 /pmc/articles/PMC9135236/ /pubmed/35617306 http://dx.doi.org/10.1371/journal.pone.0267199 Text en © 2022 Alharbi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alharbi, Majed G. Stohy, Ahmed Elhenawy, Mohammed Masoud, Mahmoud Khalifa, Hamiden Abd El-Wahed Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title | Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title_full | Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title_fullStr | Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title_full_unstemmed | Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title_short | Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
title_sort | solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135236/ https://www.ncbi.nlm.nih.gov/pubmed/35617306 http://dx.doi.org/10.1371/journal.pone.0267199 |
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