Affiliations: Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, Chios, Greece
Correspondence:
[*]
Corresponding author: Georgios Dounias, Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Street, GR-82100, Chios, Greece. E-mail:[email protected]
Abstract: This paper proposes an evolutionary computation based
approach for solving resource leveling optimization problems in project
management. In modern management engineering, problems of this kind refer to
the optimal handling 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. Standard approaches, such as
exhaustive or greedy search methodologies, fail to provide near-optimum
solutions in feasible time even for small scale problems, whereas
intelligent approaches manage to quickly reach high quality near-optimal
solutions. In this work, a new genetic algorithm is proposed which
investigates the start time of the non-critical activities of a project, in
order to optimally allocate its resources. The innovation of the proposed
approach is related to certain genetic operations applied like crossover for
the improvement of the solution quality from generation to generation. The
presentation and performance comparison of all multi-objective functions for
resource leveling that are available in literature is another interesting
part of this work. Detailed experiments with small and medium size benchmark
problems taken from publicly available project data resources produce highly
accurate resource profiles. As shown in the experimental results, the
proposed methodology proves capable of coping even with large size project
management problems without the need to divide the original problem to
sub-problems due to complexity.
