Lin, Y. H., Chua, P. C., Yin, X. F., Wang, Z., Li, N., Xiao, Z., Fu, X., & Qin, Z. (2024). Big data-driven booking consolidation and scheduling of launching service in Singapore Port. 2024 IEEE Conference on Artificial Intelligence (CAI), 272–277. https://doi.org/10.1109/cai59869.2024.00060
Abstract:
Launching services provided by launch boat (LB) operators are indispensable for vessels in port areas. In the current business practice, the operator follows a "one-trip-per-booking" method, where each booking corresponds to a LB transporting passengers to their destination. Undoubtedly, this method does not efficiently utilize the LB’s capacity. A more appealing approach is to consolidate or batch multiple service bookings into a single LB, enabling it to travel to multiple destinations within one trip. Using large-scale GPS data of LBs, we conduct data-driven analysis to gain insights into LB trajectory and traveling pattern. Based on them, we propose a real-time batching algorithm to consolidate a maximum of two bookings into a task with marginal service delay. We then address the scheduling of LBs to fulfill the consolidated tasks using rule-based real-time approaches. To validate our proposed framework, we conduct a case study in Singapore Port. The results show that after implementing the data-driven batching and scheduling algorithms, we achieve a reduction of more than 25% in the traveling distances of LBs, while maintaining a high level of service quality for passengers.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore Maritime Institute - Maritime Artificial Intelligence (AI) Research Programme (Phase 1)
Grant Reference no. : 979-8-3503-5409-6