A Genetic Algorithm for Multi-objective, Multi-mode Resource-Constrained Project Scheduling Under Uncertainty

Scheduling projects are a key objective in many models and is the recommended approach to project management. Scheduling challenges are affected by factors such as precedence relationships, resource constraints, and other constraints that affect the achievement of specified goals. These challenges depend on a variety of constraints, including precedence relationships and resource constraints, which in turn affect the achievement of part of the goals. Different strategies and patterns are adopted during project implementation to adapt to multimodal working conditions and their impact on the duration of activities. The multi-modal scheduling problem with limited resources is an optimization problem, where the main goal is to minimize the project duration while considering resource availability. The method proposed in this paper is based on project scheduling under uncertainty in time, cost and resources, using genetic algorithm (GA) as a performance improvement. The scheduling and optimization techniques developed using the Python language were also programmed to facilitate the application of this method. The results showed that the use of GA contributed to achieving optimal solutions at the global level, and the results were positive in terms of the ability to provide optimal solutions within a reasonable processing time. This method has the advantage of integrating resource planning and project scheduling even under conditions of uncertainty.