“Teachers and classes” with neural networks
International Journal of Neural Systems
Complex scheduling with Potts neural networks
Neural Computation
Case-Based Reasoning in Course Timetabling: An Attribute Graph Approach
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Recent Developments in Practical Course Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Constraint Processing
Calc/Cream: openoffice spreadsheet front-end for constraint programming
INAP'05 Proceedings of the 16th international conference on Applications of Declarative Programming and Knowledge Management
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In this paper, we describe an extension approach to the backtracking with look-ahead forward checking method that adopts weighted partial satisfaction of soft constraints that has been implemented to the development of an automated teaching assignment timetabling system. Determining the optimal solution for a teaching assignment problem is a challenging task. The objective is to construct a timetable for professors from already scheduled courses that satisfy both hard constraints (problem requirements such as no teacher should be assigned two courses at the same time) and soft constraints (teacher preferences) based on fairness principle in distributing courses among professors. The approach is done mainly to modify the variable selection method and the value assignment technique taking into account preferences and based on fairness principle. The optimized look-ahead backtracking method applied to the solution is presented and discussed along with computational results.