Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance.