In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

The rapid increase in international trade volume has caused frequent fluctuation of the vessels’ arrival time in container terminals. In order to solve this problem, this study proposes a methodology for rescheduling berth and quay cranes caused by updated information on the arrival time of vessels. A mixed-integer linear programming model was formulated for the berth allocation and crane assignment problem, and we solved the problem using a rolling-horizon approach. Numerical experiments were conducted under three scenarios with empirical data from a container terminal located in Busan, Korea. The experiments revealed that the proposed model reduces penalty costs and overall delayed departure time compared to the results of the terminal planner.

This study investigates a method for improving real-time decisions regarding the storage location of export containers while the containers are arriving. To manage the decision-making process, we propose a two module-based data-driven dynamic stacking strategy that facilitates stowage planning. Module 1 generates the Gaussian mixture model (GMM) specific to each container group for container weight classification. Module 2 implements the data-driven dynamic stacking strategy as an online algorithm to determine the storage location of an arriving container in real time. Numerical experiments were conducted using real-life data to validate the effectiveness of the proposed method compared to other alternative stacking strategies. These experiments revealed that the performance of the proposed method is robust, and therefore it can improve yard operations and container terminal competitiveness.