Joint optimisation of task abortions and routes of truck-and-drone systems under random attacks. (July 2023) - Yan, Rui; Zhu, Xiaoping; Zhu, Xiaoning; Peng, Rui
Highlights: Proposed a novel hybrid model considering drones' cluster. Characterizing the random attacks on system failure. Deriving the probability of task success and system survivability. Conducting comparative experiments to verify efficiency of algorithm. Abstract: A collaborative truck-and-drone system (TDS) can perform various tasks, such as military surveillance, reconnaissance, logistic delivery, disaster search or rescue. In order to enhance the survivability of such a system and improve the probability of task success, the task can be aborted and a rescue procedure can then be activated when a certain condition relating to malfunction or incident management is satisfied. Multiple drones can work together to complete a task with high reliability once a single drone is unable to respond to complicated emergencies. To consider this challenge, this paper designs a joint optimisation model to consider task abortion when routes of trucks and drone cluster are assumed under random attacks. Additionally, the paper considers time windows of targets and the range of the truck for protecting drones in the routines of a TDS. To minimise the expected total cost due to trucks' destruction, drones' destruction and unvisited targets, we obtain the optimal truck-and-drone routing strategy. Some numerical examples on Solomon datasets are given to illustrate the applicability of the proposed abortion strategy, present the results of sensitivity analysis on the drone cluster, and then prove the effectiveness of the optimisation method.