Robust charging station location and routing-scheduling for electric modular autonomous units
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909568156565504 |
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| author | Xia, Dongyang Yang, Lixing Lu, Yahan Azadeh, Shadi Sharif |
| author_facet | Xia, Dongyang Yang, Lixing Lu, Yahan Azadeh, Shadi Sharif |
| contents | Problem definition: Motivated by global electrification targets and the advent of electric modular autonomous units (E-MAUs), this paper addresses a robust charging station location and routing-scheduling problem (E-RCRSP) in an inter-modal transit system, presenting a novel solution to traditional electric bus scheduling. The system integrates regular bus services, offering full-line or sectional coverage, and short-turning services. Considering the fast-charging technology with quick top-ups, we jointly optimize charging station locations and capacities, fleet sizing, as well as routing-scheduling for E-MAUs under demand uncertainty. E-MAUs can couple flexibly at different locations, and their routing-scheduling decisions include sequences of services, as well as charging times and locations. Methodology: The E-RCRSP is formulated as a path-based robust optimization model, incorporating the polyhedral uncertainty set. We develop a double-decomposition algorithm that combines column-and-constraint generation and column generation armed with a tailored label-correcting approach. To improve computational efficiency and scalability, we propose a novel method that introduces super travel arcs and network downsizing methodologies. Results: Computational results from real-life instances, based on operational data of advanced NExT E-MAUs with cutting-edge batteries provided by our industry partner, indicate that charging at both depots and en-route fast-charging stations is necessary during operations. Moreover, our algorithm effectively scales to large-scale operational cases involving entire-day operations, significantly outperforming state-of-the-art methods. Comparisons with fixed-composition buses under the same fleet investment suggest that our methods are able to achieve substantial reductions in passengers' costs by flexibly scheduling units. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust charging station location and routing-scheduling for electric modular autonomous units Xia, Dongyang Yang, Lixing Lu, Yahan Azadeh, Shadi Sharif Optimization and Control Problem definition: Motivated by global electrification targets and the advent of electric modular autonomous units (E-MAUs), this paper addresses a robust charging station location and routing-scheduling problem (E-RCRSP) in an inter-modal transit system, presenting a novel solution to traditional electric bus scheduling. The system integrates regular bus services, offering full-line or sectional coverage, and short-turning services. Considering the fast-charging technology with quick top-ups, we jointly optimize charging station locations and capacities, fleet sizing, as well as routing-scheduling for E-MAUs under demand uncertainty. E-MAUs can couple flexibly at different locations, and their routing-scheduling decisions include sequences of services, as well as charging times and locations. Methodology: The E-RCRSP is formulated as a path-based robust optimization model, incorporating the polyhedral uncertainty set. We develop a double-decomposition algorithm that combines column-and-constraint generation and column generation armed with a tailored label-correcting approach. To improve computational efficiency and scalability, we propose a novel method that introduces super travel arcs and network downsizing methodologies. Results: Computational results from real-life instances, based on operational data of advanced NExT E-MAUs with cutting-edge batteries provided by our industry partner, indicate that charging at both depots and en-route fast-charging stations is necessary during operations. Moreover, our algorithm effectively scales to large-scale operational cases involving entire-day operations, significantly outperforming state-of-the-art methods. Comparisons with fixed-composition buses under the same fleet investment suggest that our methods are able to achieve substantial reductions in passengers' costs by flexibly scheduling units. |
| title | Robust charging station location and routing-scheduling for electric modular autonomous units |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2504.04408 |