Course Scheduling in an Adjustable Constraint Propagation Schema

Constraint Programming constitutes a prominent paradigm for solving time-consuming Constraint Satisfaction Problems (CSPs). In this work, at first we model a generic course scheduling problem as a CSP, that complies with the International Timetabling Competition (ITC) standards. Constraint Programming allowed us to search for a solution via several state-of-the-art methodologies and compare them. For the stochastic search methods, we propose new hybrid semi-random heuristics. Second, we chose to maintain bounds consistency during search to prune 'no-good' branches of the search tree. We theoretically define new lightweight consistency types, namely k-bounds-consistency, in order to speed up the overall search procedure. Eventually, we process real world data and show the efficiency of our proposal: While plain backtracking produces poor results, constraint propagation dramatically boosts the solutions quality, and can be 'fine-tuned' in our adjustable schema to make it even faster.