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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12108 |
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| _version_ | 1866929218446688256 |
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| author | Dötterl, Jeremias Bruns, Ralf Dunkel, Jürgen Ossowski, Sascha |
| author_facet | Dötterl, Jeremias Bruns, Ralf Dunkel, Jürgen Ossowski, Sascha |
| contents | In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data Dötterl, Jeremias Bruns, Ralf Dunkel, Jürgen Ossowski, Sascha Artificial Intelligence Machine Learning Multiagent Systems In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach. |
| title | On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2401.12108 |