Resource-constrained project scheduling: a survey of recent developments
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Branch-and-Cut Procedure for the Multimode Resource-Constrained Project-Scheduling Problem
INFORMS Journal on Computing
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences: an International Journal
Survey: Complexity of cyclic scheduling problems: A state-of-the-art survey
Computers and Industrial Engineering
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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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.