LECTURE TIMETABLING USING HEURISTIC TECHNIQUE

EMILIA BINTI ADZMI

A thesis submitted in fulfilment of the

Requirements for the award of the degree of

Bachelor of Computer Science (Software Engineering)

Faculty of Computer Systems & Software Engineering

Universiti Malaysia Pahang

JUNE, 2012

ii

ABSTRACT

The purpose of this paper is to try to develop a system about lecture timetabling

problem. This project presents about Lecture Timetabling using Heuristic Method.

Timetabling is a well known difficult combinatorial problem. In recent years, several

techniques have been used to automatically generate university timetabling problems,

including graph colouring heuristics and a lot more. Timetabling deals with the problem

of placing certain resources into a limited number of time slots, subject to given

constraints, in order to satisfy a set of stated objectives to the highest possible extent. In

this paper, by using the proposed heuristic technique, a lecture timetable is produced

considering some constraints that are able to be satisfied. Though it is impossible to

consider all constraints in one timetable, the system proposed try to satisfied some of the

constraints in order to generate the lecture timetable.

iv

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

ABSTRAK

i

ii

iii

TABLE OF CONTENTS iv-vii

LIST OF TABLES viii

LIST OF FIGURES

LIST OF ACRONYMS

ix-x

xi

LIST OF APPENDICES xii

1 INTRODUCTION

1.1 Background

1.2 Problem Statement

1.3 Project Objective

1.4 Project Scopes

1.5 Thesis Organization

1

1-2

2

3

3

4

2 LITERATURE REVIEW

2.1 Introduction

2.2 Background

5

5

5-6

v

2.3 Studies of previous work

2.3.1 A Study of Heuristic combinations for

Hyper-heuristic Systems for the

Uncapacitated

Examination Timetabling Problem

2.3.2 Extensions to the Heuristic Algorithm

for

University timetables

2.3.3 An Informed Genetic Algorithm for the

Examination Timetabling Problem

2.3.4 Solving University Timetabling

Problems

Using Advanced genetic Algorithms

2.4 Studies of techniques

2.4.1 Genetic Algorithm Based Solution

2.4.1.1 Components of genetic

algorithm

2.4.1.1.1 Representation

2.4.1.1.2 Variation Operators

2.4.1.1.3 Mutation Operator

2.4.1.1.4 Crossover Operator

2.4.1.1.5 Parent Selection

Mechanism

2.4.1.1.6 Survivor Selection

Mechanism

2.4.1.1.7 Initialization

2.4.1.1.8 Termination Condition

2.4.1.1.9 Population

2.4.1.2 Genetic Algorithm Basic

Structure

2.4.2 Metaheuristic

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16-18

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2.4.2.1. Variable Neighbourhood Search

2.4.3 Tabu Search Based hyper-Heuristic

2.5 Discussion

3 METHODOLOGY

3.1 Introduction

3.2 Rapid Application Development (RAD)

3.3 Phases RAD

3.3.1 Requirement Phase

3.3.2 Design Phase

3.3.2.1. System Design

3.3.2.2 Interface Design

3.3.2.3 Database Design

3.3.2.3.1 admin Table

3.3.2.3.2 tlecturer Table

3.3.2.3.3 tlecturerdtl Table

3.3.2.3.4 tsubject Table

3.3.2.3.5 tclassroom Table

3.3.2.3.6 classbooking Table

3.2.3 Develop

3.2.3.1 Constraints

3.2.4 Deploy

3.4 Hardware and Software requirement

3.4.1 Hardware Requirement

3.4.2 Software Requirement

3.5 Conclusion

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28-29

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4 IMPLEMENTATION

4.1 Introduction

4.2 Coding Explanation

4.2.1 Login

4.2.2 Add Lecturer

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38-39

vii

4.2.3 Update Lecturer Detail

4.3.4 Add Subject

4.3.5 Update Subject Detail

4.3.6 Display Timetable

4.3.7 Generate Timetable

40-42

43-44

45-47

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5 DISCUSSION AND CONCLUSION

5.1 Introduction

5.2 Result Analysis

5.3 System Testing

5.4. Benefits From the Project

5.5 Advantages and Disadvantages of Lecture

Timetabling using Heuristics technique

5.5.1 Advantages

5.5.2 Disadvantages

5.6 Constraints

5.7 Future Work

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6

REFERENCES

APPENDIX A

CONCLUSION

6.1 Project Conclusion

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viii

LIST OF TABLES

TABLE NO TITLE PAGE

2.2 Lecture timetabling and exam timetabling

difference

16-17

2.3

3.1

3.2

3.3

3.4

3.5

3.6

Comparison of Genetic algorithm method and

Metaheuristic method

Data dictionary of admin Table

Data dictionary of tlecturer Table

Data dictionary of tlecturerdtl Table

Data dictionary of tsubject Table

Data dictionary of tclassroom Table

Data dictionary of classbooking Table

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25

25

26

26

27

27-28

3.7 Hardware Requirement 30

3.8 Software Requirement 31-32

ix

LIST OF FIGURES

FIGURES NO TITLE PAGE

2.1

2.3

General Scheme of GA

Basic Structure of GA

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14

3.1 Flow Chart of the Heuristic Schedule Generator 21

3.2 Basic Pseudocode for Heuristic based

timetabling

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3.3

3.4

3.5

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

Use Case Diagram of Lecture Scheduling

System for FSKKP

Database Model Diagram

Example of Heuristic Pseudocode

SQL query to connect the Lecture Timetabling

System using Heuristics to database “test”

Admin Login Interface

SQL query and Coding “db_login” for login

function

Coding for Login Function

Interface for ADD lecturer

SQL Query and Coding to ADD new lecturer

Interface to UPDATE lecturer data

SQL Query and Coding for EDIT lecturer

SQL Query for DELETE lecturer

Interface for ADD subject

SQL Query and Coding to INSERT new subject

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x

4.13

4.14

4.15

4.16

4.17

Interface for ADD subject

SQL Query and Coding for connection EDIT

subject

SQL Query for DELETE subject

SQL Query to VIEW the timetable

Data type in classbooking database Table

Pseudocode of a heuristic method for

GENERATE timetable

45-46

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47

47

43

xi

LIST OF ACRONYMS

LTP Lecture Timetabling Problem

RAD Rapid Application Development

VNS

GA

UMP

FSKKP

Variable Neighbourhood Search

Genetic Algorithm

Universiti Malaysia Pahang

Fakulti Sistem Komputer dan

Kejuruteraan Perisian

xii

LIST OF APPENDIXES

APPENDIX TITLE PAGE

A Project Gantt Chart 57

1

CHAPTER 1

INTRODUCTION

1.1 Background

Due to expand enrolment of students in colleges and university in this recent

years, number of students in colleges and universities has increased hugely, some even

amount to thousands. Thus it is very difficult to take advantage of surplus resources

fully and allocate them effectively without affecting normal activities and constraints.

Constructing timetable for lecture timetable is different from constructing a timetable

for final examinations. The different between both timetabling is huge due to its great

different of constraint conditions in timetabling, so we cannot adopt the same method to

solve both of the problems [1].

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This system is developed for University Malaysia Pahang (UMP). Lecture

Timetabling Using Heuristics put forward heuristics ordering in order to construct the

timetable. By using heuristics approach, it allocates lecturer, timetabling period and

classroom resources in different phase so as to obtain a feasible solution in order to get a

solution satisfying the users.

Heuristic technique is a very versatile technique which can be change depends

on the adaptive techniques that are applied in it. Heuristic has been variously presented

in the form of proverbs, maxims, hints, suggestions, advice, principle, rules of thumb,

criteria, production rules, programs, procedures, option filters and others.

1.2 Problem Statement

The lecture timetabling problem is a common issue to all educational

institutions. Lecture timetabling problem are a very difficult issue that are still in

argument even today since we need to there are a lot constraints that we need to

consider. The examples of constraints are classroom resources, time, lecturer and other

constraints. In order to get a feasible solution, it is very important to consider all

possible constraints as much as possible. In UMP for example, ever since UMP used

open registration for its course registration, it is all the more reason for these constraints

to be considered thoroughly.

Depends on institution and constraints, some problem we can see is about the

classroom not fit for total student which means maximum total student for subject is

bigger than maximum classroom capacity and also lecturer is double booked. So, more

research of method should be done so that we can understand more about timetabling

problem to aid for a better timetable result.

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1.3 Objective

The objectives of this project are:

i. To study the way of scheduling a timetable for the lecture session of FSKKP

by using heuristic method.

ii. To design a simple and usable system.

iii. To develop a prototype to schedule the solution for a given case study.

1.4 Project Scopes

The scope of the project is as below:

i. The system is developed for FSKKP of UMP to generate the lecture

timetable.

ii. The user of the system is the staff of FSKKP.

iii. The result of the system will be used by students and the lecturers of FSKKP.

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1.5 Thesis organization

This thesis consists of 5 chapters. Chapter 1 is the Introduction. This chapter

gives a brief explanation on the introduction of system and research. The objectives of

the thesis as well as the problem statements, and scope of the project are also stated in

this chapter.

Chapter 2 is the Literature Review. This chapter briefly explains the literature

review and research for project that has been chosen. The research is divided into two

sections which the first one is the case study of an existing systems and the second one

is the research for technique that are used to develop the existing systems.

Chapter 3 will discuss on the approach used to develop the proposed system and

overall work load to develop this system. The content consists of the approach and

framework for the project that used in this system.

Chapter 4 is the implementation. This chapter will explain the process that is

involved during development of this system

Chapter 5 is the results, discussion and conclusions. The results that are

obtained from the implementation of the system are discussed thoroughly in Chapter

five. The constraints of this project are also stated clearly in Chapter five.

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This literature review chapter attempts to place an emphasis on the fundamental

aspects of the research area. It introduces the definition of the characteristics of

university lecture timetabling and specifically the university lecture timetabling

problem. Other than that, some reviews of techniques used are also discussed. This

chapter also discussed overview of some existing system.

2.2 Background

The university lecture timetabling problem consists in scheduling a set of lectures

for each subject within a given number of rooms and time periods. In general, a

university lecture timetabling problem consists in finding the exact time allocation

within a limited time period (e.g. a week), of a number of events (subject-lectures) and

also assign to them a number of resources (a lecturer, a room, etc.) in such a way that a

number of constraints are satisfied. The constraints that have to be satisfied by

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timetable are usually divided into two categories: hard constraints and soft constraints

[3].

Hard constraints are those constraints that must be satisfied or rigidly fulfilled.

Examples of constrains are, no resource (lecture, room, etc.) may be assigned to

different events at the same time, assigned resources to an event (e.g. lecturer) must

belong to the set of valid resources for that event (e.g. only specific lecturer can teach a

specific subject). The Timetables that meet the hard constraints of the problem are

referred to as feasible timetables.

On the other hand, soft constraints are those that are desirable to be fulfilled to

the possible extent, but are not fully essential. Soft constraints also can be considered as

wish lists of the characteristics that we would like the timetable to possess. Therefore,

soft constraints can also be used as optimization objectives for the search algorithm.

Some examples of soft constraints are, schedule an event within a particular period (e.g.

on evenings), minimize time gaps or travel times between adjacent lectures of the same

lecturers.

2.3 Studies of previous work

Lecture timetabling is a well research problem. This section introduced the

previous work made regarding the problem of university timetabling including

examination timetabling as well as lecture timetabling.

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2.3.1 A Study of Heuristic Combinations for Hyper-heuristic Systems for the

Uncapacitated Examination Timetabling Problem

Heuristic combinations usually take the form of a list of low-level heuristics that

are applied sequentially. This study proposes an alternative representation for heuristic

combinations, namely, a hierarchical combination of heuristics. Furthermore, the

heuristics in each combination are applied simultaneously rather than sequentially. The

study also introduces a new low-level heuristic, namely, highest cost. A set of heuristic

combinations of this format have been tested on the 13 Carter benchmarks. The quality

of the examination timetables induced using these combinations are comparable to, and

in some cases better than, those produced by hyper-heuristic systems combining and

applying heuristic combinations sequentially [4].

2.3.2 Extensions to the Heuristic Algorithm for University timetables

This research paper shows the usage of heuristics algorithm for university

timetables. It uses a heuristics algorithm in order to solve the timetabling problem in

their university. The timetables obtained in this paper show that this algorithm is suitable

for Universities and Technical Colleges since every restriction stated in the research

have been required [5]. However, the technique applied in the research might not be

suitable for all universities and college since not all university or colleges have the same

requirements and constrains.

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2.3.3 An Informed Genetic Algorithm for the Examination Timetabling Problem

This paper presents the results of a study conducted to investigate the use of

genetic algorithms (GAs) as a means of inducing solutions to the examination

timetabling problem (ETP). This study differs from previous efforts applying genetic

algorithms to this domain in that firstly it takes a two -phased approach to the problem

which focuses on producing timetables that meet the hard constraints during the first

phase, while improvements are made to these time tables in the second phase so as to

reduce the soft constraint costs. Secondly, domain specific knowledge in the form of

heuristics is used to guide the evolutionary process. The system was tested on a set of 13

real-world problems, namely, the Carter benchmarks. The performance of the system on

the benchmarks is comparable to that of other evolutionary technique s and in some

cases the system was found to outperform these techniques. The quality of the

examination timetables evolved is within range of the best results produce d in the field

[2].

2.3.4 Solving University Timetabling Problems Using Advanced Genetic

Algorithms

This paper establishes a new algorithm based on Genetic Algorithms (GA) and

sequential local search to solve course timetabling problem. In this research, they

perform preliminary experiments on standard benchmark course timetable problems.

The GA solution from the research is then compared to the manmade one produced by

the institute’s staff and the comparative results are discussed [3].

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2.4 Studies of techniques

The induction of examination timetables is a well researched field and various

artificial intelligence techniques such as Tabu search, Variable Neighbourhood Search

and evolutionary algorithms, including genetic algorithms, have been evaluated for this

purpose.

2.4.1 Genetic Algorithm Based Solution

Genetic algorithm is an evolutionary inspired by metaheuristic method that does

not guarantee to find optimal solution. There are a number of problems known as

deceptive functions that are extremely hard for genetic algorithm. However, in most of

cases, genetic algorithm can relatively quickly find a good-enough solution [6].

Genetic algorithm can be said as population-based search method. Genetic

algorithms are acknowledged as good solvers for tough problems. However, no standard

GA takes constraints into account. This chapter describes how genetic algorithms can be

used for solving constraint satisfaction problems [7]. The general scheme of a GA can be

given as follows:

Figure 2.1: General Scheme of GA

Figure 2.1: General Scheme of GA

begin

INITIALIZE population with random candidate solutions;

EVALUATE each candidate;

repeat

SELECT parents;

RECOMBINE pairs of parents;

MUTATE the resulting children;

EVALUATE children;

SELECT individuals for the next generation

until TERMINATION-CONDITION is satisfied

end

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The evaluation function represents a heuristic estimation of solution quality and the

search process is driven by the variation and the selection operator. GA has a number of

features:

i. GA is population-based

ii. GA uses recombination to mix information of candidate solutions into a new

one.

iii. GA is stochastic.

2.4.1.1 Components of genetic algorithm

The most important components in a GA consist of:

i. representation (definition of individuals)

ii. evaluation function (or fitness function)

iii. Population

iv. parent selection mechanism

v. variation operators (crossover and mutation)

vi. survivor selection mechanism (replacement)

2.4.1.1.1 Representation

Objects forming possible solution within original problem context are called

phenotypes, their encoding, the individuals within the GA, are called genotypes. The

representation step specifies the mapping from the phenotypes onto a set of genotypes.

Candidate solution, phenotype and individual are used to denotes points of the

space of possible solutions. This space is called phenotype space. Chromosome, and

individual can be used for points in the genotye space. Elements of a chromosome are

called genes. A value of a gene is called an allele.

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2.4.1.1.2 Variation Operators

The role of variation operators is to create new individuals from old ones.

Variation operators form the implementation of the elementary steps with the search

space.

2.4.1.1.3 Mutation Operator

A unary variation operator is called mutation. It is applied to one genotype and

delivers a modified mutant, the child or offspring of it. In general, mutation is supposed

to cause a random unbiased change. Mutation has a theoretical role: it can guarantee that

the space is connected.

2.4.1.1.4 Crossover Operator

A binary variation operator is called recombination or crossover. This operator

merges information from two parent genotypes into one or two offspring genotypes.

Similarly to mutation, crossover is a stochastic operator: the choice of what parts of each

parent are combined, and the way these parts are combined, depends on random

drawings.

The principle behind crossover is simple: by mating two individuals with

different but desirable features, we can produce an offspring which combines both of

those features.

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2.4.1.1.5 Parent Selection Mechanism

The role of parent selection (mating selection) is to distinguish among

individuals based on their quality to allow the better individuals to become parents of the

next generation.

Parent selection is probabilistic. Thus, high quality individuals get a higher

chance to become parents than those with low quality. Nevertheless, low quality

individuals are often given a small, but positive chance; otherwise the whole search

could become too greedy and get stuck in a local optimum.

2.4.1.1.6 Survivor Selection Mechanism

The role of survivor selection is to distinguish among individuals based on their

quality. In GA, the population size is (almost always) constant, thus a choice has to be

made on which individuals will be allowed in the next generation. This decision is based

on their fitness values, favoring those with higher quality.

As opposed to parent selection which is stochastic, survivor selection is often

deterministic, for instance, ranking the unified multiset of parents and offspring and

selecting the top segment (fitness biased), or selection only from the offspring (age-

biased).

2.4.1.1.7 Initialization

Initialization is kept simple in most GA applications. Whether this step is worth

the extra computational effort or not is very much depending on the application at hand.

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2.4.1.1.8 Termination Condition

Commonly used conditions for terminations are the following:

i. The maximally allowed CPU times elapses

ii. The total number of fitness evaluations reaches a given limit

iii. For a given period of time, the fitness improvement remains under a

threshold value

iv. The population diversity drops under a given threshold.

Note: Premature convergence is the well-known effect of loosing population diversity

too quickly and getting trapped in a local optimum.

2.4.1.1.9 Population

The role of the population is to hold possible solutions. A population is a multiset

of genotypes. Most of GA applications have a constant population size which means

they doesn’t change during the evolutional search.

2.4.1.2 Genetic Algorithm Basic Structure

A simple genetic algorithm describes the following cycle:

1st Assessment of each individual of the population;

2nd

Verification of the termination criteria;

3rd

Generation of random n chromosomes that form the initial population;

4th

If verify termination criterion - cycle ending;

5th

Selection of n/2 pairs of chromosomes for crossover;

6th

Reproduction of chromosomes with recombination and mutation;

7th

New population of chromosomes called new generation;

8th

Go back to step 2.