A GENERALIZED HEURISTIC LEARNING APPROACH TO PROJECT SCHEDULING PROBLEMS WITH RESOURCE CONSTRAINTS

ABSTRACT

We present a generalized heuristic learning algorithm and a solution approach for its implementation in solving project scheduling problems with resource constraints. The search process of the algorithm is characterised by the complete heuristic learning process: state selection, heuristic learning, and search path review. The heuristic learning process enables the algorithm to continue to improve the state selection decision. The heuristic learning threshold of the algorithm allows users to specify solution quality, optimal or near-optimal solutions, with efficient computation. The implementation approach is based on the dynamic nature of activity status and resource availability of a project. It consists of states, state transition operator, heuristic estimate, and the cost of transition between states. The performance analysis of this algorithm with Patterson's 110 problem is presented.

Keywords:

• Project scheduling
• Heuristic learning
• Backtracking
• Artificial intelligence