Evolutionary computation for resource leveling optimization in project management

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.