Population Learning Algorithm for Resource-Constrained Project Scheduling

The paper proposes applying the population-learning algorithm to solving a single mode resource constrained project scheduling problem with makespan minimization as an objective function. The paper contains problem formulation and a description of the proposed implementation of the population learning algorithm (PLA). To validate the approach a computational experiment has been carried. It has involved 1440 instances from the available benchmark data set. Experiment results show that the proposed PLA implementation is an effective tool for solving single mode resource constrained project scheduling problems. In a single run the algorithm has produced solutions with mean relative error value well below 1% as compared with available upper bounds for benchmark problems.