Resource-constrained project scheduling with ant colony optimization algorithm

Abstract

Resource allocation commonly becomes one of the critical problems in project scheduling. This issue usually occurs
because project managers estimate the schedule of activities and network time without considering resource availability.
Resource-Constrained Project Scheduling Problem (RCPSP) links to the allocation of resources or set of resources into certain
activities in order to accomplish particular objectives. Various approaches have been performed to overcome RCPSP, including
the heuristic approach. In this research, Ant Colony Algorithm is used to solve RCPSP. There are 11 examples of projects being
investigated with dissimilarity in-network and several activities. The implementation of the Ant Colony Algorithm resulted in
the percentage of a near-optimal solution of 63.64%. Besides, the duration obtained from the algorithm above the manual
scheduling (assumed optimal) was only 4.29%. Sensitivity analysis was performed to understand how substantially the changes
of ACO parameters influenced the result obtained from the algorithm. Based on the result, it could be concluded that the
parameters of ACO have no significant effect to project duration.