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The hard constraint in uncapacitated examination timetabling is usually represented by the following:
The main soft constraint in uncapacitated examination timetabling is as follows:
Figure 1 shows the pseudocode for the constructing the proximity coefficient matrix and calculating the penalty cost for each solution in uncapacitated examination timetabling problems.
First, GA is used to solve the uncapacitated examination timetabling problem of Carter benchmark dataset.
Hertz (1991) stated that approaches developed to solve timetabling problems usually consist of two phases.
In the context of the exam timetabling benchmark data sets used in this research, this sequencing strategy has been implemented by Carter et al.
Since then, the use of graph based heuristic orderings has been extended to other types of timetabling problem.
Kiaer and Yellen (1992) modeled a course timetabling problem as a weighted graph.
Because of that, in our distributed timetabling system all the known types of hard and soft constraints can be expressed or the system can be easily extended.
A course in timetabling problem can be regular (it belongs to only one student/student group) or it can be shared between many student groups.
We think that this system gives contribution to the development of distributed timetabling systems and it can be used as a base for future research.
A MultiAgent Architecture for Distributed Course Timetabling, Proceedings of 5th International Conference on the Practice and Theory of Automated Timetabling (PATAT), pp.