Development of Web based Timetabling System

Olivia Kembuan, Gladly Caren Rorimpandey, Parabelem Tinno Dolf Rompas

and Julyeta Paulina Amelia Runtuwene

Department of Informatics, Universitas Negeri Manado, Minahasa, North Sulawesi, Indonesia

Keywords: Web, Timetabling, Genetic Algorithm, Php, Mysql.

Abstract: This paper presents a case-study which applies genetic algorithm to Department of Informatics, Manado

State University timetabling system. The currently timetabling method is not efficient to solve the problem.

The aim of this research was to develop a web based timetabling system to optimize the resources using

genetic algorithm. The algorithm was tested with the real data containing 47 combined lessons data to be

scheduled into 40 timeslots and 8 rooms. The research method used is a design or experimental method.

Rapid Application Development (RAD) in system development life cycle (SDLC) model was used as the

system development methodology of this Timetabling system. PHP Programming language and MySQL

were used in this timetabling application. The results showed that the proposed timetabling system was

successfully minimize processing time and provide the optimal solution for the problem.

1 INTRODUCTION

Timetabling scheduled courses or examinations is a

fundamental activity in educational institutions,

these must be assigned into appropriate timeslots for

students, lecturers, and classrooms subject to

constraints (Burke et al, 2007).

There are several categories of timetabling in

educational institution and these include master

timetabling, lecture timetabling, faculty timetabling,

examination timetabling, etc (Momodu and

Omogbhemhe, 2014). Of all the various types,

examination and lecture timetabling has more

difficulties than others during the timetabling

process because of the number of constraint and

resources involved.

This research concentrates on the planning lecture

timetable in the university.

This system presents an approach to interactive

timetabling used by Department of Informatics,

Manado State University timetabling system. There

are 4 student groups, 47 combined lessons data to be

scheduled into 40 timeslots and 8 rooms, with no

constraint with the room. However, for some certain

courses, there are also laboratory works to be

scheduled differently. It is also making the time

table harder. The details of the task mentioned that it

has the requirements of each (constraint) that must

be met, this causes the scheduling process to be

complex in its implementation.

Carter et al. (1996) introduced various searching

methods on constraint satisfaction problems and

demonstrated that this technique could be applied to

optimization problems. Landa (2004), explained

some of the steps that must be taken in order to

create an error-free timetabling in educational

institutions. The steps includes: (1) every college

needs to send a request for a certain number of days

and time slots to the administrative unit; (2) the

institution timetable officer from the administrative

unit will allocate each college a certain number of

days and available exam venues for each college is

based on the combination of the request number,

availability of resources and previous experience.

Experience has shown that there are never enough

resources to accommodate the demands of all the

college s and departments. The timetable officer also

reserves a number of “spare” slots for emergencies;

(3) each college then come up with a feasible

timetable using the resources they have been

assigned. Some colleges, further break down these

resources to departmental level and produce an

assembled timetable for the entire departments in

that college.

Although this research made it clear that this

timetabling system experienced several setbacks like

any form of error for a large number of courses.

Kembuan, O., Rorimpandey, G., Rompas, P. and Runtuwene, J.

Development of Web based Timetabling System.

DOI: 10.5220/0009010403170322

In Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology (EIC 2018), pages 317-322

ISBN: 978-989-758-411-4

317

Also the process is time consuming and takes

several weeks to resolve which can slow down the

academic activities if not well handled.

The currently timetabling system is very time

consuming and resources optimization problems

occur due to insufficient room resources and lab

facilities. This method is not only inefficient in

terms of time but also requires precision in the

process because there are no error messages that

indicate the occurrence of class collisions or

mismanagement of lecture time. In addition, this

process is very susceptible to errors in its

implementation which, if an error occurs, can cause

problems in the lecture process in the future.

We identified the necessity of an automated

timetabling system. The problem with the

timetabling system itself, that it has a lot of variation

according with the policy of the institution. The

preparation of lecture schedules in the Informatics

Engineering Study Program includes determining

the number of classes opened, allocating lecture

halls and practice rooms, determining lecturers,

determining the length of the lecture, determining

the start and end hours of lectures, and determining

the day of lecture.

This study aims to find a more accurate solution

in the form of web-based lecture scheduling

software by applied the Genetic Algorithms. This

algorithm used computation using the principle of

biological evolution modelling that can provide

positive feedback to provide optimum results in

finding solutions. This application is expected to

help in scheduling lectures more efficiently, as well

as minimizing the occurrence of errors that usually

occur in the process of designing class schedules

that are done manually.

This paper will be divided into four main parts.

The first part discusses about some related works

and about genetic algorithm in solving scheduling

problem. The second part will be proposed the

methodology that used. The third part will be

architecture design of the system and discussion

after implementing the system. The last part will be

closed by the conclusion and also some suggestions

to improve the system. PHP Programming language

and MySQL were used in this timetabling

application. The result showed that the proposed

timetabling system was successfully minimize

processing time and provide the optimal solution for

the problem.

2 A GENETIC APPROACH TO

THE TIMETABLING

PROBLEM

A Genetic Algorithm is based on populations of

solutions. Most genetic algorithms operate on a

population of solutions rather than a single solution.

The genetic algorithm generates other solutions,

which tend to be better, by combining chromosomes,

i.e. solutions, using three genetic operators that are

fundamental for selection, crossover and mutation.

The genetic search begins by initializing a

population of individuals. Initially a population is

created by some mechanism. Then Individual

solutions are selected from the population, then mate

to form new solutions. The mating process, typically

implemented by combining, or crossing over,

genetic material from two parents to form the

genetic material for one or two new solutions,

confers the data from one generation of solutions to

the next. Random mutation is applied periodically to

promote diversity. If the new solutions are better

than those in the population, the individuals in the

population are replaced by the new solutions. Use of

a genetic algorithm requires the definition of

initialization, crossover, and mutation operators

specific to the data type in the genome.

In developing a genetic algorithm, we must have

in mind that its performance depends largely on the

careful design and set-up of the algorithm

components, mechanisms and parameters. This

includes genetic encoding of solutions, initial

population of solutions, evaluation of the fitness of

solutions, genetic operators for the generation of

new solutions and parameters such as population

size, probabilities of crossover and mutation,

replacement scheme and number of generations.

Genetic Algorithm itself takes long time to be

executed and requires a certain machine

configuration. This can be a problem for execution

time. The second limit of the algorithm is the

importance of the random part. Due to a huge set of

solutions, the algorithm cannot guaranty to get the

best result or the achievement of a certain level of

fitness.

2.1 Initialization

The initialization process is done by giving the

initial values of the genes with random values

according to predetermined limits.

In our research approach, inside the

chromosome, there is a gene for each activity in the

EIC 2018 - The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and

Application on Green Technology

318

timetable system. This gene represents the course

schedule time of the activity. So, a chromosome is

actually an array of genes, each gene representing

the course schedule, room and time.

Set parameters of genetic algorithm including

population size, maximum iteration, mutation

probability and crossover probability. Then encode

an initial solution into a chromosome. Repeat this

step until the number of individual equals to the

population size.

To speed up the computation time, we restrict the

value of maximum generation with maximum value

2.2 Selection

Selection is another important factor to consider in

implementing genetic algorithm. It is a procedure to

select offspring from parents to the next generation.

According to the general definition, the selection

probability of a chromosome should show the

performance measure of the chromosome in the

population. Hence a parent with a higher

performance has higher probabilities of being

selected to next generation (Chen et al, 2008).

After generated Chromosome that has the highest

fitness, this chromosome has highest probability to

be selected for next generation chromosomes. We

are used random number to selection process.

2.3 Crossover

Crossover is an operation to generate a new

chromosome (i.e. child or offspring) from two

parents. It is the main operator of Genetic

Algorithm. The parent chromosome is chosen

randomly and the number of chromosomes that are

crossed is affected by the crossover_rate (ρc)

parameter. The pseudo code for crossover process is

given in Figure 1.

K = kromoson

cr = crossover_rate

Figure 1: Crossover Pseudo-code.

2.4 Mutation

Mutation is used to produce perturbations on

chromosomes in order to maintain the diversity of

population. The number of chromosomes that

mutated in one population is determined by the

mutation_rate parameter. The mutation process is

done by replacing gene that is randomly selected

with a new value that is randomly obtained. (Chen et

al, 2008).

First we must calculate the total length of gen in

The population. In this case the total length

generation is calculated by multiplying the number

of generation in Chromosome with number of

population.

3 METHOD

The research method used is a design or

experimental method. Rapid Application

Development (RAD) in system development life

cycle (SDLC) model was used as the system

development methodology of this Timetabling

system.

RAD allows users to participate in an iterative

design and development process. Conceptually, the

project “loops” through the Design, Construction,

and Acceptance phases, followed by re-Design,

revised Construction, Acceptance, and so on. The

RAD approach has advantages, since it usually

achieves results quickly, the design is less abstract,

and users have assurance that up-to-date

requirements are considered. Its disadvantages

include difficulty in controlling the process and

ensuring the creation of an acceptable product.

Because, it was having precise requirements and

well understood milestones. Detail requirement

begin:

k← 0;

while (k<populasi) do

R[k] ← random(0-1);

if (R[k] < cr ) then

select Chromosome[k] as

parent;

end;

k = k + 1;

end;

Development of Web based Timetabling System

319

analysis was conducted at each different user

category getting help of admin of the current

timetable management system. RAD Stages

explained in Figure 2.

Figure 2: RAD Stages (Source from www.credata.com).

RAD uses two types of methodology [Alan D],

Phased Development and Prototyping.

3.1 Stages of RAD

The Rapid Application Development path may be

adapted to different CASE tools and development

environments. This section briefly describes the four

stages of RAD (Alan, 2009).

• Requirements Planning.

In this stage, there are some techniques that are

used to investigate requirement. There are

background reading, interviewing, and

observation.

As soon this project started, an interview was

set up with the Head of Informatics

Department and academic staff, which

provided me some internal and useful

information of the scheduling system in

Department of Informatics.

• User Design.

In this stage the key users, meeting in

workshops, decompose business functions and

define entity types associated with the system.

They complete the analysis by creating action

diagrams defining the interactions between

processes and data.

In this stage, prototypes of timetabling system

are developed and shown the users for review

and revision. A plan for implementing the

system is prepared.

• Construction.

In the Construction stage, a small team of

developers, working directly with users,

finalizes the design and builds the system.

At this stage, we started developing programs

using php and implementing genetic

algorithms in it. We also prepared

documentation necessary to operate the

proposed timetabling system.

• Implementation.

The implementation stage involves

implementing the new system.

Necessary adjustments to the hardware and

system software configurations are completed,

and instructions given to the academic staff

who will be operating the system.

This may include implementing bridges

between existing and new systems, converting

data, and training users. User acceptance is the

end point of the implementation stage.

4 EXPERIMENTAL RESULTS

Currently used data and method are in general based

on observation and interviewed result. There are 40

time slots ranges day from Monday to Friday at time

07:30 to 13.55. In the lecture data there is a

combination of course data, name of lecturer, and 4

class groups. There are 8 lecture rooms that can be

used with a capacity of up to 50 students.

Due to available resources and the necessity of a

web based automated system by campus; we used

PHP server scripting language for coding process.

The design phase is carried out to determine how

the system will be operated. This relates to

determining the hardware, software, network,

program display, forms and reports that will be used.

In addition, it is also necessary to specify the

programs, databases and files needed. This system is

database using MySQL. Accessing databases using

php MyAdmin, Apache servers and web browsers.

4.1 Database Design

Database is the platform used to store data in most

information systems which stores the data. Database

design follows a sequential order. These include the

inflow schema, Relational data model (Figure 3).

The database was designed using MYSQL due to the

level of functionality database can offer.

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Application on Green Technology

320

Figure 3: Relational Data Model.

File Design is a collection of interconnected

records, where the file can be manipulated. In

making the physical design (detail table) of a

Relational Database scheme (RD), take precedence

on the table containing on the side: 1) One (1), One

or Many (1 / N), and 3) Many (N). So in designing

the database for this Scheduling System there are or

use 9 (nine) files, namely 1) tb_day, 2) tb_hour, 3)

tb_time, 4) tb_course, 5) tb_lecturer, 6) tb_class, 7)

tb_schedule, 8) tb_room , 9) tb_result.

Timetabling application that is designed to have

one user access rights, administrator. The first output

of this system is the login form. On the login page,

the administrator/user can log in to the system by

supplying the right username and password. On that

same page (the login page) there exists a link to the

password retrieval page. The screen output is shown

in Figure 4. Below.

There are several management menus that can be

done by the user, such as: insert lecture time data,

lecturer data, course data, detailed lecture data,

generating schedules and viewing scheduling results.

(Figure 5 and Figure 6).

There are three variables in this timetabling

problem, namely kuliah (course schedule), ruang

(room), and waktu (time).

Figure 4: Login Page.

Figure 5: Management Page.

Below is the output page of the system which is

university examination timetable showing the

courses, the time and the respective examination

venues obeying all given constraints. In the output

page of timetabling system, there is a sorting feature

based on day, time, subject, number of credits, class,

lecture room, and lecturer name.

Figure 6: View Schedule Page.

In our experiment we generated data with

execution time averaged 68, 55 seconds and an

average memory usage of 10487.73 kb. The

effectiveness of the resulting time cannot be

compared to the usual manual scheduling. The

manual scheduling process usually takes more than

one week to process.

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5 CONCLUSIONS

This paper describes part of a research project with

the objective of developing a timetabling system for

Department Informatics, Manado State University.

Based on the research that has been done, it can be

concluded that the applications that have been

developed are expected to optimize and improve the

efficiency of the lecture scheduling process in the

Department of Informatics that was previously done

manually. The result showed that the proposed

timetabling system was successfully can produce

schedule without class collision, minimize

processing time which are provide the optimal

solution for the problem.

In the future, we plan to continue with further

application of the timetabling system at Manado

State University. We have already started to work on

the course timetabling problem for the Faculty of

Engineering, where there is an intent to have the

system used by all departmental schedule deputies

right from the start.

ACKNOWLEDGMENTS

Our thanks go to the team of the Department of

Informatics at Manado State University who helped

us to get necessary data. I express my sincere thanks

to Mrs. Gladly Rorimpandey, Mr. Soenandar Milian

Tompunu Tengker, and Ms. Merry Liow for their

encouragement that made this paper possible.

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Application on Green Technology

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