Multi-project flexible resource profiles project scheduling with Ant Colony Optimization

In today's rapidly evolving management world, the scheduling of multiple projects where each one's execution depends on another's successful completion, is of great importance. This paper presents a hybrid meta-heuristic algorithm composed of an external Genetic Algorithm (GA) and an encapsulated Ant Colony Optimization (ACO) algorithm for the flexible resource constrained multi-project scheduling problem (MPFRCPSP). The proposed idea is grounded on the concept of prioritizing the sub-projects' scheduling based on: a) the number of external (to other sub-projects) relations and b) the resource requirements as compared to the resource shortage for each resource type and each sub-project. The implementation uses the Genetic Algorithm to deal with the classification and prioritization of the projects to be scheduled and the inner ACO algorithm, to perform the activity list optimization for each project. The proposed method was validated using a consistent number of PSP Lib[l] data sets.