Abstract.
Besut Campus of Universiti Sultan Zainal Abidin is having a problem to arrange class
schedule because it was done manually. The timetabling problem at university is a
non-deterministic polynomial time hard (NP-hard problem) under multiple constraints
and limited resources. The example of NP-hard problem such as facilities, lecturer,
lecture, capacity of class and others
The system is using Genetic Algorithm (GA) method in order to solve the problem.
Genetic algorithm mimics the process of natural selection and can be used to solve
a complex problem. These methods to find an optimal solution of solving timetable
by using a real student data of Faculty Informatics and Computing.
schedule because it was done manually. The timetabling problem at university is a
non-deterministic polynomial time hard (NP-hard problem) under multiple constraints
and limited resources. The example of NP-hard problem such as facilities, lecturer,
lecture, capacity of class and others
The system is using Genetic Algorithm (GA) method in order to solve the problem.
Genetic algorithm mimics the process of natural selection and can be used to solve
a complex problem. These methods to find an optimal solution of solving timetable
by using a real student data of Faculty Informatics and Computing.
Introduction.
The process of making class timetable for university is a complex process since it involves many requirements and constraints such as capacity of class, lectures and rooms to be considered as a part of the requirements
The purpose of this system is to help UniSZA Campus Besut to auto-generated schedule their classes offered during a semester in available classrooms. The advantages of using this system is it help to avoid a redundant time (class/room allocation) table at one time.
The main objective of this system is to design a better Class Timetable Allocation that can fulfil all the requirement needed by the users.
The purpose of this system is to help UniSZA Campus Besut to auto-generated schedule their classes offered during a semester in available classrooms. The advantages of using this system is it help to avoid a redundant time (class/room allocation) table at one time.
The main objective of this system is to design a better Class Timetable Allocation that can fulfil all the requirement needed by the users.
Objectives.
1
To develop the web – based class timetable allocation system that able to schedule the class timetable in Besut Campus. |
2
To design a system for Class Timetable Allocation that can full fill all the requirement needed by the users. |
3
To test either the system can fully be functioning where the system can manage to handle and generated the timetable without any redundant class. |
Methodology.
FRAMEWORK
BPA and Faculty need to login into the system.
When Faculty login, they need to give input to the system which is programme,
subjects, lecturer and also lecture.
The BPA need to full fill the facilities and BPA details while login.
All the information will be stored in the database will be used to
generate the timetable.
The BPA will generate the timetable. The Genetic Algorithm will be i
mplement in the generated timetable process.
ALGORITHM
Genetic algorithm mimics the process of natural selection and can be used as a technique for solving complex optimization problems which have large spaces .
Genetic Algorithms (GAs) are a specialization of evolution programs, based on the principals of natural selection and random mutation from Darwin biological evolution.
Basic operators such as selection, mutation and crossover are applied to get the best results involving the new generation of population.
Genetic Algorithm(GA) Technique.
The first step of GA is it will initialize the population of creatures. Every creature will be given a random fitness function which is
the value is used to rank a particular solution against all solution.
Fitness value are assign to each solution depending how close it is actually to optimal solution of the problem. They will
place the new offspring in new population and will use new generated population for further run of genetic algorithm.
Next, is selecting step. Two parent will be choosing from a population according to their fitness function. The two parents
will undergo crossover process which is offspring are created by exchanges between parents. After that, mutation will take over.
the value is used to rank a particular solution against all solution.
Fitness value are assign to each solution depending how close it is actually to optimal solution of the problem. They will
place the new offspring in new population and will use new generated population for further run of genetic algorithm.
Next, is selecting step. Two parent will be choosing from a population according to their fitness function. The two parents
will undergo crossover process which is offspring are created by exchanges between parents. After that, mutation will take over.
Results.
Conclusion.
1
A more efficient and reliable class timetable can be achieved |
2.
The class allocation system able to handle and generated the timetable without any redundant class. |
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