IT WORKSHOP-III PROJECT Project Members: Sandeep Kakani Mudit Agrawal Under the guidance of Prof. Kamal Karlapalem

Motivation University Time-Tabling has many constraints.  Resource Conflict - A teacher must not have two classes in the same slot  Electives - Two electives taken by the same student cannot be allocated in the same slot  Exclusion - A period of more than one slot must be contiguous.  Juxtaposition - Different outputs with same data enables to compare the better result.

motivation (…continued) Accuracy and patience needed, manually it is a combustive process. Automated Time-Table generation reduces a lot of work. Different Time-Tables for the same data can be obtained - - juxtaposition. Automation makes it reliable too.

 The approach taken is “Schedule By Batch”.  A batch is chosen  Each subject of that batch is allocated  For each subject, first its lectures are allocated, then the tutorials and finally labs.  For each data chosen a slot is randomly picked. A new slot may be required in the following cases: Slot is busy with another compulsory course Teacher for that subject is not free. Classroom of required strength is not free. Methodology

After all the subjects are allocated, the electives are taken into consideration.  The chosen slot must not have a regular subject for that batch.  An elective can be allocated in a slot which is already having another elective except in the following cases: Both the electives are not taken by the same student. Both the electives are not taught by the same teacher.  All the electives in a slot are stored as an array of subjects. Methodology (…continued)

First batch is taken, along with lec | tut | lab of a subject of that batch Timetable for One Batch A slot is chosen at random It is checked whether the teacher is free or not A clash as the same teacher cannot teach in two batches at the same time S1(4) Timetables for different batches E1 E2 Clash if the same student is enrolled in Both E1 and E2 electives A Slot Mon Tue Wed Thu Fri Sat P1 P2 P3 P4 A sample timetable S2(4)

Methodology (…continued) Submission of data required depends on many aspects of the institute. The user is interactively asked to input the following details:  Number of subjects in each batch.  Subject ids and corresponding teacher ids( for lec, tut and labs)  Duration of each lec.( tut or lab) for each period in the week.  No. of combinations of electives in each batch.  All the combinations of electives for each batch.  Lecture, tutorial and lab timings for each period of each elective. All the data inputted gets stored into a file - to aid the user to generate different outputs redirecting the same input from that file.

Q. What is No- Solution Problem? Answer :A no-solution problem occurs when the program cannot find any further suitable slots which satisfy the preferences and constraints given for the next subject. Dealing with No Solution Problem.  A count checks the total clashes occurring per allocation.  If it crosses a limit => it flushes out the information of all the batches in the time-table and starts reallocation.  Hence NO-SOLUTION problem is taken care of. Methodology (…continued)

 The output gives the tabular form of the timetable for each batch with each subject entry in a slot.  Dialog has been used to provide a better view of the output(in standard 800 X 600 resolution or higher).  Screen-shot of the output follows: Conclusion

Though the timetable depends much on the internal complexity of the courses in a university yet the software tends to make up for most of those. E.g.  Inadequate teachers => more clashes per slot  Probability for a solution decreases for increase in clashes. The timetable project helped us to visualize the complicacies of a time-table and also showed us the path to remove those by generating a software for it. Conclusion(…continued)