Optimization of a Naval Asset Maintenance Plan through Hybrid Evolutionary Algorithms and Swarm Intelligence

The majority of naval industry companies use operation and maintenance plans for their equipment, systems and assets. However, because they are not optimized, such naval operation and maintenance plans are not practical when put into execution, either because they do not plan adequate time gaps between maintenance, or because they do not estimate changes in shipbuilding stages and in available infrastructure. This work addresses an optimization problem with a large solution search space for maintenance and operation plans of naval assets of the Brazilian Navy in which evolutionary computing and swarm intelligence are employed to solve it. It involves the construction of two to six warships over a span of more than half a century. The constraints and parameters used were not found in the literature. The results of the evolutionary model and the combination of genetic and swarm operators are novel, and prove that the proposed model yields improved and viable maintenance and operation plans compared to that obtained by previously used techniques, such as Monte Carlo Simulation. The project's executable java file can be found at: https://github.com/TiagoPaulinelli/Optimized-Naval-Asset-Plan-with-EA-and-PSO