iv
TABLE OF CONTENT PAGE
DECLARATION APPROVAL
DEDICATION ABSTRACT
i ABSTRAK
ii ACKNOWLEDGEMENT
iii TABLE OF CONTENT
iv LIST OF TABLES
viii LIST OF FIGURES
x LIST OF APPENDICES
xii LIST OF ABREVIATIONS
xiii LIST OF PUBLICATION
xiv CHAPTER
1. INTRODUCTION 1
1.1 Research Background
1 1.1.1 Inventory Management Problem
3 1.1.2 Implementation of Computing Technology in Industries
5 1.1.3 Challenge in Designing Production Quantity Estimation Model
6 1.2
Research Problem 9
1.3 Research Question
10 1.4
Research Objectives 10
1.5 Research Scope
11 1.6
Significance of Study 11
1.7 Organization of the Thesis
12
2. LITERATURE REVIEW 13
2.1 Inventory Management
13 2.1.1 Reasons on Having Inventory
14 2.1.2 Type of Inventory
14 2.1.2.1 Inventory based on function
14 2.1.2.2 Inventory based on production process
15 2.1.3 Properties of Inventory
16 2.1.4 Type of Costs
17 2.1.5 Inventory Modelling
19 2.2
Artificial Neural Network 23
v 2.2.1 Concept of Artificial Neural Network
23 2.2.2 Neural Network Model
25 2.2.3 Neural Network Architecture
27 2.2.4 Learning Algorithm
30 2.2.5 Neural Network Back Propagation Supervised Learning
31 2.2.6 Previous Research on Artificial Neural Network in Industry
35 2.3
Optimization Technique 37
2.3.1 Genetic Algorithm 38
2.3.1.1 Procedures of Genetic Algorithm 39
2.3.1.2 The Components of Genetic Algorithm 40
2.3.1.3 Artificial Neural Network and Genetic Algorithm 45 ANN-GA
2.3.1.4 Previous Study of Artificial Neural Network and 48
Genetic Algorithm ANN-GA 2.3.2 Particle Swarm Optimization
49 2.3.2.1 Concept of Particle Swarm Optimization
50 2.3.2.2 Parameter Particle Swarm Optimization
54 2.3.2.3 Artificial Neural Network and Particle Swarm
55 Optimization ANN-PSO
2.3.2.4 Previous Study on Artificial Neural Network and 58
Particle Swarm Optimization ANN-PSO 2.4
Shortcoming of Previous Research in Inventory Problem 59
2.5 Summary
61
3. RESEARCH METHODOLOGY 62
3.1 General Steps of the Research Methodology
62 3.2
Reality Problem Situation and Company Profile 64
3.3 Conceptual Model
66 3.4
Scientific Model 67
3.4.1 Research Tools 69
3.4.2 Preliminary Dataset Analysis 70
3.4.3 Data Transformations Normalization 72
3.4.4 K-Fold Cross-validation 73
3.4.5 Design of Neural Network Back Propagation Model 74
vi 3.4.5.1 Process Designing NNBP Model in Rapid Miner
78 3.4.6 Design Hybrid Neural Network Based on Genetic
80 Algorithm HNNGA Model
3.4.6.1 Process Designing HNNGA Model in Rapid Miner 85
3.4.7 Design Hybrid Neural Network Based on Particle 88
Swarm Optimization HNNPSO Model 3.4.7.1 Process Designing HNNPSO Model in Rapid Miner
90 3.4.8 Performance Measurement
93 3.4.8.1 Root Mean Square Error RMSE
94 3.4.8.2 Mean Absolute Error MAE
94 3.5
Solution 94
3.5.1 Experiment and Method Test 95
3.5.1.1 Training Cycle 96
3.5.1.2 Learning Rate 96
3.5.1.3 Momentum 96
3.5.1.4 Hidden Layer and Hidden Nodes 96
3.5.2 Evaluation and Validation 97
3.6 Summary
99
4. EXPERIMENT AND RESULT 100
