Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910401385463808 |
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| author | Kikuta, Daisuke Ikeuchi, Hiroki Tajiri, Kengo Toyama, Yuta Nakamura, Masaki Nakano, Yuusuke |
| author_facet | Kikuta, Daisuke Ikeuchi, Hiroki Tajiri, Kengo Toyama, Yuta Nakamura, Masaki Nakano, Yuusuke |
| contents | As a telecom provider, our company has a critical mission to maintain telecom services even during power outages. To accomplish the mission, it is essential to maintain the power of the telecom base stations. Here we consider a solution where electric vehicles (EVs) directly supply power to base stations by traveling to their locations. The goal is to find EV routes that minimize both the total travel distance of all EVs and the number of downed base stations. In this paper, we formulate this routing problem as a new variant of the Electric Vehicle Routing Problem (EVRP) and propose a solver that combines a rule-based vehicle selector and a reinforcement learning (RL)-based node selector. The rule of the vehicle selector ensures the exact environmental states when the selected EV starts to move. In addition, the node selection by the RL model enables fast route generation, which is critical in emergencies. We evaluate our solver on both synthetic datasets and real datasets. The results show that our solver outperforms baselines in terms of the objective value and computation time. Moreover, we analyze the generalization and scalability of our solver, demonstrating the capability toward unseen settings and large-scale problems. Check also our project page: https://ntt-dkiku.github.io/rl-evrpeps. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_02448 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief Kikuta, Daisuke Ikeuchi, Hiroki Tajiri, Kengo Toyama, Yuta Nakamura, Masaki Nakano, Yuusuke Optimization and Control Artificial Intelligence Machine Learning Multiagent Systems As a telecom provider, our company has a critical mission to maintain telecom services even during power outages. To accomplish the mission, it is essential to maintain the power of the telecom base stations. Here we consider a solution where electric vehicles (EVs) directly supply power to base stations by traveling to their locations. The goal is to find EV routes that minimize both the total travel distance of all EVs and the number of downed base stations. In this paper, we formulate this routing problem as a new variant of the Electric Vehicle Routing Problem (EVRP) and propose a solver that combines a rule-based vehicle selector and a reinforcement learning (RL)-based node selector. The rule of the vehicle selector ensures the exact environmental states when the selected EV starts to move. In addition, the node selection by the RL model enables fast route generation, which is critical in emergencies. We evaluate our solver on both synthetic datasets and real datasets. The results show that our solver outperforms baselines in terms of the objective value and computation time. Moreover, we analyze the generalization and scalability of our solver, demonstrating the capability toward unseen settings and large-scale problems. Check also our project page: https://ntt-dkiku.github.io/rl-evrpeps. |
| title | Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief |
| topic | Optimization and Control Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2404.02448 |