Fast enumeration of effective mixed transports for recommending shipper collaboration
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917972524662784 |
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| author | Kira, Akifumi Terajima, Nobuo |
| author_facet | Kira, Akifumi Terajima, Nobuo |
| contents | In this study, we focus on a form of joint transportation called mixed transportation and enumerate the combinations with high cooperation effects from among a number of transport lanes registered in a database (logistics big data). As a measure of the efficiency of mixed transportation, we consider the reduction rate that represents how much the total distance of loading trips is shortened by cooperation. The proposed algorithm instantly presents the set of all mixed transports with a reduction rate of a specified value or less. This algorithm is more than 7,000 times faster than simple brute force. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast enumeration of effective mixed transports for recommending shipper collaboration Kira, Akifumi Terajima, Nobuo Computer Science and Game Theory In this study, we focus on a form of joint transportation called mixed transportation and enumerate the combinations with high cooperation effects from among a number of transport lanes registered in a database (logistics big data). As a measure of the efficiency of mixed transportation, we consider the reduction rate that represents how much the total distance of loading trips is shortened by cooperation. The proposed algorithm instantly presents the set of all mixed transports with a reduction rate of a specified value or less. This algorithm is more than 7,000 times faster than simple brute force. |
| title | Fast enumeration of effective mixed transports for recommending shipper collaboration |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2503.24179 |