Solving project scheduling problems with a heuristic learning algorithm

We have developed an approach to implement a real time admissible heuristic search algorithm to solve project scheduling problems. This algorithm is characterised by the complete heuristic learning process: state selection, heuristic learning, and search path review. This implementation approach is based on the dynamic nature of the activity status and the resource availability of a project. It consists of states, state transition operator, heuristic estimate, and the cost of transition between states. The performance analysis shows that the accumulation of heuristic learning during the search process has led to the re-scheduling of resource dominating activities, which is a major factor in controlling the overall project completion time.