University Timetable Scheduling using particle Swarm Optimization

1. MENTOR : HIRANMAY SAMMADAR
GROUP MEMBERS:
ABHINAV KUMAR(103010)
PIYUSH KUMAR CHAUHAN(103001)
KHUSBOO KUMARI(103002)
ASHISH MISHRA(103022)
D A P A R T M E N T O F C O M P U T E R S C . & E N G G .
UNIVERSITY TIMETABLE
SCHEDULING USING PARTICLE
SWARM OPTIMIZATION
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2. Problem definition
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 University timetable scheduling using particle swarm
optimization
It is a NP-Hard problem and we solve this problem using
particle swarm optimization

3. What is P and NP ?
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 P is set of problems that can be solved in polynomial time
 NP (nondeterministic polynomial time) is the set of
problems that can be solved in polynomial time by a
nondeterministic computer

4. NP-Complete Problems
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We will see that NP-Complete problems are the “hardest”
problems in NP:
 If any one NP-Complete problem can be solved in polynomial
time…
 …then every NP-Complete problem can be solved in polynomial
time…
 …and in fact every problem in NP can be solved in polynomial
time (which would show P = NP)

5. What is NP-Hard ?
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 Definition of NP-Hard
A set of problems which is converted to a particular
problem but that particular problem is not converted
to any other problem of that set.

6. Constraints
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 Common type of constraints:-
Time assignment
Room capacities
Number of laboratories

7. Hard constraints
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 No student attends more than one period at the same time.
 Only one lecture is taking place in each room at a given
time.
 No teacher should be taking two classes at the same point
of time.
 Minimum of one laboratory assistant should be present in
each laboratory session.
 Minimum of two laboratories should be there in every
section of the Basic Sciences & Humanities Department.

8. Soft constraints
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 All the laboratory sessions in a week should be scheduled
in the first half.
 A lecturer wants his lecture to be delivered in the last
period.
 Swapping of the periods.

9. What is Particle Swarm Optimization ?
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 A simple, computationally efficient optimization method
 population-based, stochastic search
 based on a social-psychological model of social influence and
social learning
 individuals follow a very simple behavior: emulate the success of
neighboring individuals
 emergent behavior: discovery of optimal regions in high
dimensional search spaces

10. Geometrical Illustration
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Position updates
xi (t + 1) = xi (t) + vi (t + 1)
Velocity update per dimension:
vij (t + 1) = vij (t) + c1r1j (t)[yij (t) − xij (t)]
+ c2r2j (t)[ˆyj (t) − xij (t)]

11. Front End
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 The front end is design by using core java.
The user gives the details about following :-
 Number of classroom
 Number of laboratory
 Number of department
 Number of subject
 Number of faculty

12. Back End
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 The back end is created with the help of DBMS.
We use the following table for creating the database.
 Teacher( t_name,t_id,designation,dept,address,phno.)
 Subject( s_name,s_code,no._of_lecture,sem,dept)
 Classroom(room_id,capacity)
 Laboratory(lab_name, lab_id,capacity,dept_lab)
 Department(dname,dept_id,hod_name,no_of_lab,no._of_teacher,
no_of_room)
 Teacher(t_id,t_sub)

13. Architecture of University Timetable System
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