Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918138128367616 |
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| author | Duc, Tran Trung Minh, Vu Duc Doanh, Nguyen Ngoc Nguyen, Pham Gia Ghaoui, Laurent El Hoang, Ha Minh |
| author_facet | Duc, Tran Trung Minh, Vu Duc Doanh, Nguyen Ngoc Nguyen, Pham Gia Ghaoui, Laurent El Hoang, Ha Minh |
| contents | The Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options addresses fleet optimization incorporating both conventional charging stations and continuous wireless charging infrastructure. This paper extends Schneider et al.'s foundational EVRP-TW model with arc-based dynamic wireless charging representation, partial coverage modeling, and hierarchical multi-objective optimization prioritizing fleet minimization. Computational experiments on Schneider benchmark instances demonstrate substantial operational benefits, with distance and time improvements ranging from 0.7% to 35.9% in secondary objective components. Analysis reveals that 20% wireless coverage achieves immediate benefits, while 60% coverage delivers optimal performance across all test instances for infrastructure investment decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options Duc, Tran Trung Minh, Vu Duc Doanh, Nguyen Ngoc Nguyen, Pham Gia Ghaoui, Laurent El Hoang, Ha Minh Systems and Control The Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options addresses fleet optimization incorporating both conventional charging stations and continuous wireless charging infrastructure. This paper extends Schneider et al.'s foundational EVRP-TW model with arc-based dynamic wireless charging representation, partial coverage modeling, and hierarchical multi-objective optimization prioritizing fleet minimization. Computational experiments on Schneider benchmark instances demonstrate substantial operational benefits, with distance and time improvements ranging from 0.7% to 35.9% in secondary objective components. Analysis reveals that 20% wireless coverage achieves immediate benefits, while 60% coverage delivers optimal performance across all test instances for infrastructure investment decisions. |
| title | Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.07402 |