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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13085 |
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| _version_ | 1866909965770293248 |
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| author | Nakao, Haruko Ma, Tai-Yu Connors, Richard D. Viti, Francesco |
| author_facet | Nakao, Haruko Ma, Tai-Yu Connors, Richard D. Viti, Francesco |
| contents | Electrifying demand-responsive transport systems need to plan the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. Earlier studies assume a fully electrified fleet and overlook the planning issue in the transition period. This study addresses the joint fleet size and charging infrastructure planning for a demand-responsive feeder service under stochastic demand, given a user-defined targeted CO2 emission reduction policy. We propose a bi-level optimization model where the upper-level determines charging station configuration given stochastic demand patterns, whereas the lower-level solves a mixed fleet dial-a-ride routing problem under the CO2 emission and capacitated charging station constraints. An efficient deterministic annealing metaheuristic is proposed to solve the CO2-constrained mixed fleet routing problem. The performance of the algorithm is validated by a series of numerical test instances with up to 500 requests. We apply the model for a real-world case study in Bettembourg, Luxembourg, with different demand and customised CO2 reduction targets. The results show that the proposed method provides a flexible tool for joint charging infrastructure and fleet size planning under different levels of demand and CO2 emission reduction targets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal mixed fleet and charging infrastructure planning to electrify demand responsive feeder services with target CO2 emission constraints Nakao, Haruko Ma, Tai-Yu Connors, Richard D. Viti, Francesco Optimization and Control Electrifying demand-responsive transport systems need to plan the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. Earlier studies assume a fully electrified fleet and overlook the planning issue in the transition period. This study addresses the joint fleet size and charging infrastructure planning for a demand-responsive feeder service under stochastic demand, given a user-defined targeted CO2 emission reduction policy. We propose a bi-level optimization model where the upper-level determines charging station configuration given stochastic demand patterns, whereas the lower-level solves a mixed fleet dial-a-ride routing problem under the CO2 emission and capacitated charging station constraints. An efficient deterministic annealing metaheuristic is proposed to solve the CO2-constrained mixed fleet routing problem. The performance of the algorithm is validated by a series of numerical test instances with up to 500 requests. We apply the model for a real-world case study in Bettembourg, Luxembourg, with different demand and customised CO2 reduction targets. The results show that the proposed method provides a flexible tool for joint charging infrastructure and fleet size planning under different levels of demand and CO2 emission reduction targets. |
| title | Optimal mixed fleet and charging infrastructure planning to electrify demand responsive feeder services with target CO2 emission constraints |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.13085 |