Big data-driven booking consolidation and scheduling of launching service in Singapore Port

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.