DESIGN OF EXAMINATION TIMETABLING SOFTWARE USING GENERIC ALGORITHMS AND SIMULATED ANNEALING

The difficulties of developing appropriate examination time table for institutions and tertiary is increasing. Institutions are enrolling more students into wider variety of courses in many different fields. For example, at Osun State Polytechnic, Iree, approximately 14,000 students have to be filled into about 150 exams over two and a half week’s period.
The examination timetable problem regards the scheduling for the exams of a set of polytechnic courses, avoiding overlaps of exams of courses having common students, and spreading the exams for the students as much as possible.
Examination scheduling (timetabling) is a very important process in education institutions. The main challenge is to schedule examinations to timeslots and rooms over a specific period while satisfying a set of constraints. The previous attempts were based on the graph coloring concept. In this case the vertices represent causes, and join two vertices only if they cannot be scheduled at the same time. The problem is therefore to find the chromatic number of resulting graph.
One of the major approaches in exam timetabling over the years has been constraint programming approach (Simulated Annealing). This method constraint programming logic language (chip) also to solve exam timetabling problem. Constraint programme phase provide an initial solution and a simulated annealing phase to improve the quality of solutions.
The local search approaches play an important role in the exam timetabling literature. White and Xie and Di Gaspero and Schaef used tabu search method in exam timetabling. White and Xie Kept two table lists, the used short-term table list, and the long-term tabu lists keeps tracks of the most moved exams, Di Gaspero and Scheaf used a single table list, but when exams are added to this list, its for a randomly determined number of iterations.
The polytechnic has two semesters per each academic year. Each semester per each academic year. Each semester is made up of up to fifteen weeks of teaching, by followed by two weeks of examination. There are two examination sessions per day except on Saturday where student are allowed to rest and start of Sabbath for seventh day Adventist. Examinations are mostly three hours long with a few exceptions which deviates for half an hour or two hours.
There are an increasing number of courses which cut across facilities and polytechnics-wide courses which are offered to more than a thousands students at the same time. The problem is also complicated by the freedom of choices by students on optimal courses, where students have wide range of choices which cut across department and faculties.
The Examination Timetable Problem (ETP) is usually modeled as an NP-Hard (non-deterministic polynomial time hard problem) combinatorial optimization problem. The problem demands that a given number of exams are scheduled in a limited number of periods and venues in such a way that no student will have more than one exam at a time and other constraint are satisfied.
Although consideration will be based in particular on exams timetabling, the ideas presented here can be extended to many other application, which include not only other scheduling problems but also multi-criteria problems. In general, the reason to present an application to exams time tabling is justified by the affiliation of the auditors and their awareness of the increased difficulty that some recent strategies have introduced in this academic task. Just as an example monitoring the tendency forwards the flexibility of curricular and the increase of the number of students enrolled in each course.

1.2 Statement of the Problem
In this project work, we present a new solution method for examination timetabling, consisting of two phase: a generic algorithm phase to improve provide an initial solution and a simulated annealing phase to improve the quality of solution. The simulated annealing applies kempe chain neighborhood and includes a mechanism that allow the user to define a certain period of time in which the algorithm should run. We perform preliminary experiments of the algorithm on the real data set from the OSUN STATE POLYTECHNIC, IREE.
However, the main different between the two approach is that our simulated annealing phase is equipped with more refine mechanisms that help to determine crucial cooling schedule parameter.

1.3    Aim and Objective of the Study
The principle aim of this project aim of this project work is develop examination timetabling software that will be useful to our education institutions, using Generic Algorithms and simulated annealing.
The following are the set objective

1.4 Limitation of the Project
The project is developed to cover the fixing of examination timetables for all students in this institution of Osun State Polytechnic, Iree but it can be implemented in any other tertiary institution, this can be achieved by merely adjusting the input design of the program.

1.5 Scope of the study
In this project, attention is focused in formulating mathematical models for the examinations timetable at Osun State Polytechnic, Iree. This will act as a benchmark for testing  heuristic algorithms (describe an algorithm that modifies itself in response to the user) and help future reformations of the problem models. The Examination Timetabling Problems (ETP) differ considerably from the polytechnic curse scheduling problem.

1.6 Significant of the project
The significant of the research work is to assist to curb the examination timetable problems that may arise in the future, based on the following concussion and recommendation made by the researcher.
The finding of this study will enable us to understand and the importance of good examination timetable system for the tertiary institution easy and effective also with the used of appropriate examination timetable tom stimulate the timetable of various resources combination so as to encourage  better timetable planning and information gathering.
To minimize the length of examination period with the constraint given to used rate determine every student academic performance and also to allocate inugolator to time and venues.