An Enhanced ACO Algorithm for Multi-objective Maintenance Scheduling of Oil Tanks

In this paper, an enhanced ant colony optimization algorithm (EACOA) is proposed for the multi-objective maintenance scheduling of oil tanks. In the algorithm, tabu search is incorporated into ant colony optimization. Through a decreasing probability function, the proposed algorithm improves ant colony optimization is easy to trap in local optimum and hence the optimal solution to the scheduling problem can be effectively achieved. Experimental results demonstrated the effectiveness and feasibility of the algorithm for the scheduling problem considered. The Pareto-optimal solutions found effectively help decision-makers arrange maintenance scheduling tasks.