An AIA-ACO Hybrid Algorithm of Resource Leveling for Aviation Project

摘要

Aviation project which always involves too many resources need to do resource leveling for enhancing the utilization of resources so as to reducing project cost. Intelligent optimization algorithms and heuristic approaches are inefficient and inflexible when solving large-scale aviation resource leveling problems. A hybrid algorithm based on Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO) is proposed to overcome those drawbacks. This hybrid algorithm is used to optimize project network plan consists three main phases: at first stage, a solution space is established for hybrid algorithm; at second stage, AIA is used to search sub-optimal solutions; at third stage, with the information of these sub-optimal solutions, ACO can find the final solution effectively. The feasibility and effectiveness of the algorithm are validated by an example.