A hybrid genetic approach for multi-objective and multi-platform large volume surveillance problem. (1st January 2013) - Dridi, Olfa; Krichen, Saoussen; Guitouni, Adel
Efficient management of surveillance assets and successful scheduling of surveillance tasks are complex decision-making problems for the execution of large volume surveillance missions in order to improve security and safety. A mission can be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task assignment rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. Finding efficient management solutions should be investigated to optimise assets-resources allocation and tasks scheduling. In this paper, we propose to model this optimisation problem as a multi-objective, multi-platform assignment and scheduling problem. Resources are to be assigned to accomplish different tasks. Surveillance tasks should be scheduled into successive periods. The problem is designed to consider two conflicting objective functions: minimising the makespan and minimising the total cost. As the problem is NP-hard, a hybrid genetic algorithm (HGA) is proposed. The empirical validation is performed using a simulation environment called Inform Lab, and a comparison to two state-of-the-art multi-objective approaches based on selected performance metrics. The experimental results show that HGA performs consistently well for high dimensional problems.