Real-time RL-based Matching with H3 Geohash Partitioning in Smart Freight Platform
This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight transportation sector. The framework utilizes the H3 geospatial indexing system for accurate positioning and employs a pre-filtering mechanism to streamli...
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| Published in: | IEEE Vehicular Technology Conference pp. 1 - 7 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
07.10.2024
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| ISSN: | 2577-2465 |
| Online Access: | Get full text |
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| Abstract | This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight transportation sector. The framework utilizes the H3 geospatial indexing system for accurate positioning and employs a pre-filtering mechanism to streamline the matching process. When evaluated on a simulated model of Montreal's transportation network, the framework demonstrates promising results in generating matches that reduce travel distance and prioritize timely service. Through extensive experimentation, a configuration utilizing ReLU activation was identified as particularly efficient, even under limited computational resources. This research contributes to the development of advanced, real-time matching algorithms in logistics and show-cases the potential of integrating reinforcement learning with geospatial analysis to address complex transportation challenges. These findings offer valuable insights for freight companies seeking to improve their matching processes, potentially leading to cost reductions and enhanced service quality. |
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| AbstractList | This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight transportation sector. The framework utilizes the H3 geospatial indexing system for accurate positioning and employs a pre-filtering mechanism to streamline the matching process. When evaluated on a simulated model of Montreal's transportation network, the framework demonstrates promising results in generating matches that reduce travel distance and prioritize timely service. Through extensive experimentation, a configuration utilizing ReLU activation was identified as particularly efficient, even under limited computational resources. This research contributes to the development of advanced, real-time matching algorithms in logistics and show-cases the potential of integrating reinforcement learning with geospatial analysis to address complex transportation challenges. These findings offer valuable insights for freight companies seeking to improve their matching processes, potentially leading to cost reductions and enhanced service quality. |
| Author | Shiri, Ali Lamghari, Amina YarAhmadi, Asad Keivanpour, Samira |
| Author_xml | – sequence: 1 givenname: Ali surname: Shiri fullname: Shiri, Ali email: Ali.Shiri@polymtl.ca organization: Polytechnique Montreal,Department of Mathematics and Industrial Engineering,Canada – sequence: 2 givenname: Asad surname: YarAhmadi fullname: YarAhmadi, Asad email: Asad.Yarahmadi@polymtl.ca organization: Polytechnique Montreal,Department of Mathematics and Industrial Engineering,Canada – sequence: 3 givenname: Samira surname: Keivanpour fullname: Keivanpour, Samira email: Samira.Keivanpour@polymtl.ca organization: Polytechnique Montreal,Department of Mathematics and Industrial Engineering,Canada – sequence: 4 givenname: Amina surname: Lamghari fullname: Lamghari, Amina email: Amina.Lamghari@uqtr.ca organization: Université du Québec à Trois-Rivières,Department of Management,Canada |
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| Snippet | This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight... |
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| SubjectTerms | Companies Computational modeling Costs Deep Q-Learning Freight Transportation Geospatial analysis Indexing Learning-based algorithms Logistics Real-time Matching Real-time systems Reinforcement Learning Smart Transportation Transportation Vehicle dynamics Vehicular and wireless technologies |
| Title | Real-time RL-based Matching with H3 Geohash Partitioning in Smart Freight Platform |
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