Hybrid nature-inspired intelligence for the resource leveling problem

The paper deals with a class of problems often met in modern project management under the term resource leveling optimization problems. The problems of this kind refer to the optimal allocation of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, controlling related budgeting and finances and managing the construction production line. For the effective resolution of resource leveling optimization problems, the use of nature inspired intelligent methodologies is proposed. Traditional approaches, such as exhaustive or greedy search methodologies, often fail to provide near-optimum solutions in a short amount of time, whereas the proposed intelligent approaches manage to timely achieve high quality near-optimal solutions. In the paper, extensive experimental results are presented, based on available data collections existing in literature for a number of known benchmark project management problems. The comparative analysis of three different intelligent metaheuristics, shows that a hybrid nature inspired intelligent approach, combining ant colony optimization and genetic algorithms, proves to be the most effective approach in the majority of benchmark problems and special decision making settings tested.