Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908377221693440 |
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| author | Zheng, Zubin Liu, Shengcai Ong, Yew-Soon |
| author_facet | Zheng, Zubin Liu, Shengcai Ong, Yew-Soon |
| contents | With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pickup-delivery, and partial recharges (EVRP-TW-SPD), which has widespread real-world applications. We propose a hybrid memetic algorithm (HMA) for solving EVRP-TW-SPD. HMA incorporates two novel components: a parallel-sequential station insertion (PSSI) procedure for handling partial recharges that can better avoid local optima compared to purely sequential insertion, and a cross-domain neighborhood search (CDNS) that explores solution spaces of both electric and non-electric problem domains simultaneously. These components can also be easily applied to various EVRP variants. To bridge the gap between existing benchmarks and real-world scenarios, we introduce a new, large-scale EVRP-TW-SPD benchmark set derived from real-world applications, containing instances with many more customers and charging stations than existing benchmark instances. Extensive experiments demonstrate the significant performance advantages of HMA over existing algorithms across a wide range of problem instances. Both the benchmark set and HMA are to be made open-source to facilitate further research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19580 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges Zheng, Zubin Liu, Shengcai Ong, Yew-Soon Neural and Evolutionary Computing With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pickup-delivery, and partial recharges (EVRP-TW-SPD), which has widespread real-world applications. We propose a hybrid memetic algorithm (HMA) for solving EVRP-TW-SPD. HMA incorporates two novel components: a parallel-sequential station insertion (PSSI) procedure for handling partial recharges that can better avoid local optima compared to purely sequential insertion, and a cross-domain neighborhood search (CDNS) that explores solution spaces of both electric and non-electric problem domains simultaneously. These components can also be easily applied to various EVRP variants. To bridge the gap between existing benchmarks and real-world scenarios, we introduce a new, large-scale EVRP-TW-SPD benchmark set derived from real-world applications, containing instances with many more customers and charging stations than existing benchmark instances. Extensive experiments demonstrate the significant performance advantages of HMA over existing algorithms across a wide range of problem instances. Both the benchmark set and HMA are to be made open-source to facilitate further research in this area. |
| title | Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2410.19580 |