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AUTOMATED SYSTEM FOR SOLVING SCHOOL TIME TABLE TABLE
CHAPTER ONE
1.1
INTRODUCTION
The class
timetabling problem is a typical scheduling problem that appears to be a
stressful job in every academic institute. In previous years, timetable
scheduling was done manually with a single person or group of individuals
involved in the task of scheduling it manually. Planning of timetable is one of
the most complex and error-prone applications because it is actually done
manually. This situation demands a comprehensive approach where a computer can
be used to schedule a timetabling problem by being automated using a concept
gotten from evolutional biology called Genetic algorithm.
1.2 BACKGROUND OF STUDY
Scheduling is one of the important tasks that we encountered
in our daily life situations. There are various types of scheduling problems
which includes personnel scheduling, production scheduling, educational timetable
scheduling etc.
In educational timetable scheduling, there are many
constraints that need to be satisfied in order to get a clear solution which
has made it a very hard task. Educational timetable scheduling can be called a
non-polynomial hard (NP hard) which means that, there are no exact algorithms
that can solve this problem of timetable scheduling. Hence, evolutionary
techniques have been used to solve the time table scheduling problem.
Techniques like Evolutionary Algorithms (EAs), Genetic Algorithms (GAs) etc.
Scheduling
conflicts arise in different varieties of settings as illustrated by the
following examples:
(i) Consider a
school environment that requires the scheduling of a given set of courses and
meetings between students and lecturers. Each course will take place in a
particular lecture hall and each hall has its own capacity. We must also make
sure that no student or lecturer is fixed up in more than one particular
appointment.
(ii) Consider a factory that produces different sorts of
gadgets. Each gadget must first be processed by a “machine 1”,“machine
2”,“machine 3” and so on where different gadgets requires different amount of
processing time on different machines.
(iii)
Consider the central processing unit of a computer that must process a
sequence of jobs that arrive over time.
Genetic Algorithms (GA)
This is a procedure that is used to find an appropriate
solution to search problems through the application of evolutionary biology.
These kind of algorithm uses biological techniques such as natural selection,
mutation, genetic inheritance and sexual reproductions (recombination or cross
over), along with Genetic programming (GP) to solve problems. Genetic
algorithms are primarily executed using computer simulations in which an
optimization problem is specified. For this problem, members of a space called
Candidate solutions are represented using abstract representations called
chromosomes. The GA consists of an iterative process that evolves a working set
of individuals called a Population towards a fitness function or an objective
function.
The evolutionary process of a GA is a simplified and stylized
simulation of the biological version. The starting point is the population of
individuals randomly generated according to the probability distribution
usually informs and updates this population in steps called Generations. Each
generation of multiple individuals are randomly selected from the current
population based on some application of fitness using crossover and modified
through mutation to form a new population.
Crossover: - This is the process of exchanging Genetic
materials (substrings), donating rules, and structural components, features of
a machine learning, search, or optimization problem.
Selection: - this is the process of applying the fitness
criteria to choose which individuals from a population will go on to reproduce.
Replication:-The propagation of individuals from one generation
to the next generation.
Mutation: - it is said to be the sudden change in the
composition of a gene or the modification of chromosomes for single
individuals.
Theory of Genetic Algorithm: The theory consists of two main
approaches.
They are as follows; Markov chain analysis and Schema theory.
The Markov chain is primarily concerned with characterizing the stochastic
dynamics of a GA system. i.e the behavior of the random sampling mechanism of a
GA over time. The highest limitation of this approach is that while crossover
is easy to implement, its dynamics are difficult to describe mathematically.
Markov chain analysis of simple GAs has therefore been more successful at
capturing the behavior of evolutionary algorithms with selection and mutation
only.
Time table
In institutions, the class time table is a major
administrative activity which is prerequisite.
The time table problem or conflict can be said to be the
problem of assigning a number of events into a limited number of time period.
Wren defines timetable as follows “Timetable is the allocation of subject to
constraints of given a objects being in
space time in such a way as to satisfy as nearly as possible a set of desirable
objectives”, Wren A.(1995).The problem of
the time table is subject to many constraints which are usually divided
into two categories: “hard” and “soft”.
Hard Constraints:
These are constraints that must be enforced. Some examples of
such constraints are:
(iv)
In each period, there should be sufficient resources (e.g. rooms and
lecturers) available for all the events that have been scheduled for that time
period.
(v) No lecturer should have different classes at the same
time slot. There cannot be more than two classes for a subject in one day.
Soft Constraints
Soft constraints are those that are desirable but not
absolutely essential. Sometimes it is impossible to satisfy all soft
constraints in real world situations. Some of the soft constraints (in both
exams and course timetabling) are:
(vi)
Lecturers and students may prefer to have all their lectures in some
number of days and to have a number of lecture-free days
(vii)
Lab classes may not be in consecutive hours
(viii)
Every staff should get at least one first hour
(ix)
A particular class may need to be scheduled in a particular time period.
1.3 STATEMENT OF PROBLEM
Any problem has a set of valid results. It is said to form
the solution space. In an optimization problem, the main aim or goal is to find
results that maximize or minimize a set of criteria. If we look at the solution
space as an n-dimensional space then essentially we are searching for a global
minima or maxima in the solution space. The Genetic Algorithm is a type of
algorithm for searching the solution space and finding maxima or minima, though
not necessarily the global maxima or minima.
Timetable scheduling is always said to be a complex
optimization problem which has shown to be related to the clique of
minimization problem which is called NP complete. In such kind of problem where
no efficient algorithm is known, it is ideal to apply genetic algorithm to such
kind of problem which is used for search a solution space. It is necessary to
realize that such scheduling is a world problem that has an immediate
application in various forms of timetabling including, examinations, public
transport and roster, though in no way limited to.
1.4 AIMS AND OBJECTIVES
The project is a software application that many Institutions,
businesses and some companies may actually need. This is a simple case of an
allocation problem.
(1) The project
involves developing a program that can schedule time table effectively for
school. The prototype of this work should be followed by the development of a
booking system that can automatically allocate resources. These resources are
allocated automatically using a Genetic Algorithm.
(2) The main
principal of this project is to solve timetable problems with evolutionary
computing processes and more specifically using Genetic algorithms.
(3) The actual
different between this project with other one existing in the faculty of
science is that the timetable does not clash and it is more efficient and
simple to schedule using the idea gotten from Genetic algorithm.
1.5 SIGNIFICANCE OF STUDY
This project is a
topical one demanding a research effort due to conflict that recently occurred
in my school. Recently a junior lecturer from the department of computer
science was having a lecture with us and a senior lecturer from another faculty
walked in and said that we should leave the class because he want to use the
class for another lecture and so the class discontinue because of the lecture.
Many more of these types of instances has happen and so need an urgent
attention so that a good learning environment can be achieved.
1.6 LIMITATION OF THE STUDY
The lists of
constraint on this project are so many but just the few major ones will be
listed:
(x) To start
with, the project took a lot of time to understand, researched on before
embarking on it.
(xi) Unavailability
of electric power supply during the research work.
(xii)
The location where this research was performed was not good enough in
terms of network signals strength which is usually on the poor side.
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