a multi-objective bisexual reproduction genetic algorithm for ...

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studies many different courses, these courses also have to be scheduled to different
times. The other constraints are also satisfied.
TABLE 2-3 Sample timetable
Course Section Time Day Classroom Lecturer
CSC211 1 13:00-16:00 W B304A01 00020
CSC211 2 8:00-11:00 W B304A01 00020
CSC221 1 10:00-12:00 T B304A05 00012
CSC210 1 13:00-16:00 M B304A02 00012
CSC110 1 9:00-12:00 F B304A02 00015
CSC113 1 13:00-16:00 T B304A05 00023
2.2 The Related Works on Course Scheduling Problems
Course scheduling is a multi-dimensional NP-Complete problem that has
generated hundreds of papers and thousands of researchers who have attempted to
solve this problem. In this section, we discuss some of the primary approaches that
have been applied to general course scheduling problems, scheduling for courses and
exams. In practice, the main idea used for the course scheduling can be applied to
exam scheduling and vice versa. The approaches can be divided into four groups:
sequential methods, cluster methods, constraint based methods, and meta-heuristic
methods [9].
2.2.1 Sequential Methods
Sequential methods order the events for scheduling using heuristics (often graph
coloring heuristics). They assign the ordered events to valid time periods so that no
events in the period are in conflict with each other, i.e. two events which require the
same resource are not scheduled in the same time period [10].
The graph coloring approach usually presents events as different vertices with
an edge between the two vertices where two respective events conflict in some way.
The graph coloring is the process of allocating different colors to each vertex so that
no two adjacent (conflicting) vertices have the same color.