The paper deals with "resource leveling optimization problems", a class of problems that are often met in modern project management. The prob- lems of this kind refer to the optimal handling of available resources in a candi- date 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 resource leveling optimization in problem analysis, evolutionary intelligent me- thodologies are 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 quickly reach high quality near-optimal solutions. In this paper, a new genetic algorithm is proposed for the investigation of the start time of the non-critical activities of a project, in order to optimally allocate its resources. Experiments with small and medium size benchmark problems taken from publicly available project da- ta resources, produce highly accurate resource profiles. The proposed metho- dology proves capable of coping with larger size project management problems, where conventional techniques like complete enumeration is impossible, obtain- ing near-optimal solutions.
['Christos Kyriklidis', 'Georgios Dounias']
Application of Evolutionary Algorithms in Project Management