vii
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
DEDICATION
ACKNOWLEDGEMENT
ii
iii
iv
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
v
vi
vii
x
xi
LIST OF ABBREVIATIONS
LIST OF SYMBOLS
LIST OF APPENDICES
xiii
xiv
xvi
INTRODUCTION
1.1
Introduction
1.2
Research background
1
1
2
1.3
1.4
1.2.1
Job Shop Problem (JSP)
1.2.2
Ant Colony Optimization (ACO)
Problem Statement
Objective of the Study
2
3
5
5
1.5
1.6
1.7
Scope of the Study
Significance of the Study
Thesis Organization
6
6
7
LITERATURE REVIEW
2.1
Introduction
2.2
Behaviour of Real Ants
2.3
2.4
2.5
Classical Job Shop Problem
Computational Complexity
Shifting Bottleneck
8
8
8
11
13
13
viii
2.6
2.7
2.8
3
Review Relevant Research
Research Findings on ACO
Summary
RESEARCH METHODOLOGY
3.1
Introduction
19
19
3.2
Job Shop Problem
3.2.1
Introduction
3.2.2
Definition for Job Shop
3.2.3
JSP Mathematical Formulation
19
19
20
20
3.2.4
3.2.5
3.2.6
3.2.7
3.2.8
22
23
24
26
27
3.3
Constraint for the JSP
Assumptions for the JSP
The JSP Representation
Problem Formulation
Graph Representation for Job Shop Problem
The Solution Approaches
3.3.1
Methodology of Ant Colony Optimization
3.3.2
The new ACO-based Algorithm
3.3.2.1 Initialization
3.3.2.2
3.3.2.3
3.3.2.4
3.3.2.5
3.4
4
5
14
17
18
The Ant Architecture
Daemon Action
Pheromone Update
Terminating Condition
Summary
30
32
34
36
36
38
38
40
40
INDUSTRIAL PROBLEM EXPLORATION
41
4.1
4.2
4.3
4.4
Introduction
Introduction to Manufacturing Company
The Manufacturing Framework
Company Problem Description
41
41
42
47
4.5
4.6
4.7
The Case Study
Sensitivity Analysis
Summary
51
59
67
IMPLEMENTATION OF ANT COLONY OPTIMIZATION
FOR JOB SHOP PROBLEM
68
5.1
Introduction
68
5.2
Development of ACO Algorithm
68
ix
5.3
5.4
5.5
6
7
Algorithm for ACO
Data Diagram
Summary
70
84
89
SYSTEM DEVELOPMENT FOR ACO SOFTWARE
6.1
Introduction
90
90
6.2
6.3
6.4
6.5
Program Fundamentals
Programming with Microsoft Visual Studio
The Visual Studio Application
Program Visualization
90
91
92
93
6.6
Summary
104
ANALYSIS OF RESULTS, CONCLUSION AND RECOMMENDATION
7.1
Introduction
7.2
Results
7.3
Analyze of The Results
105
105
105
109
7.4
7.5
7.6
113
114
114
REFERENCES
Appendices A – 7
Conclusion
Contribution
Recommendation for Future Research
115
?? – ??
x
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
2.2
2.3
The Instance data with 3 machines and 3 jobs.
Relevant Research on Ant Colony Optimization .
Relevant Research on Job Shop Problem.
12
15
16
2.4
Relevant Research on Job Shop Scheduling Problem in Ant
Colony Optimization.
17
3.1
The Processing Time for 2/3/G/Cmax job shop problem.
29
4.1
4.2
The Processing Time for 2/3/G/Cmax job shop problem.
Random values of Cmax and makespan depending on the
57
parameter values of α, β and ρ.
Random values of Cmax and makespan depend on the values of
β parameters, when ρ = 0 .
Random value of Cmax and makespan depending on the values
60
of β parameters, when ρ = 1 and α = 0.
Random values of Cmax and makespan depend on the values of
β parameters, when ρ = 2.
Random values of Cmax and makespan depend on the values of
β parameters, when ρ = 1 and α = 1.
62
4.3
4.4
4.5
4.6
4.7
4.8
Random values of Cmax and makespan depend on the values of
ρ parameters, when β = 0.
Random values of Cmax and makespan depend on the values of
ρ parameters, when β = 1.
5.1
Short caption
7.1
7.2
7.3
The Stimulation Results.
Results for makespan with 6 machines.
Comparison the results between Shifting Bottleneck Heuristic
Method [1] with the new ACO-based Algorithm.
61
63
64
65
66
85
110
110
112
xi
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
2.1
2.2
2.3
Ant experimental for the bridge experiment.
The ants are moving on the straight line.
An unexpected obstacle has interrupted the initial path.
9
9
10
2.4
2.5
The same situation on the other side of the obstacle.
Visual of ants choosing the shorter path.
10
11
3.1
3.2
3.3
The definition of a 2/3/G/Cmax job shop problem into graph.
The result for a 2/3/G/Cmax job shop problem into graph.
The flow chart for work explanation.
28
30
32
3.4
The problem solving step.
33
4.1
4.2
4.3
The framework for the warehouse process.
The typical job shop layout problem that forms the case study.
The data for the product customized E07 rack.
43
44
48
4.4
4.5
4.6
4.7
4.8
The processing time for the product.
The data input with 6 machine.
The graph for input data Power Press proses.
The graph for input data Tapping process.
The graph for input data Clinching Process.
49
50
52
53
54
4.9
4.10
4.11
The graph for input data Bending Process.
The graph for input data Machining Process.
The total number of edges versus total number of jobs run on 6
machines.
55
56
4.12
57
4.13
4.14
4.15
The total number of edges versus total number of nodes and
operations.
The graph on sensitivity analysis for Table 3.
The graph for Sensitivity analysis for Table 4.4.
The graph for sensitivity analysis for Table 5.5.
58
61
62
63
4.16
4.17
The graph for sensitivity analysis for Table 6.
The graph for sensitivity analysis for Table 7.
64
65
xii
4.18
The graph for sensitivity analysis for Table 4.8.
66
5.1
5.2
5.3
The Pseudocode of ACO algorithm.
The framework for the proposed ACO algorithm.
The Gantt Diagram for the Power Press Process.
68
70
86
5.4
5.5
5.6
5.7
The Gantt Diagram for the Tapping Process.
The Gantt Diagram for the Clinching Process.
The Gantt Diagram for the Bending Process.
The Gantt Diagram for the Machining Process.
86
87
88
88
6.1
6.2
6.3
6.4
The Welcome GUI for the program.
The Project Properties for the Case Study.
The Input Data for the CNC Machining.
The Input Data for the Bending process.
93
94
95
96
6.5
6.6
6.7
6.8
6.9
The Input Data for the Clinching process.
The Input Data for the Tapping process.
The Input Data for the Power Press process.
The Results for the case study.
The Debug for the case study.
97
98
99
100
101
6.10
The flow run for the software development.
103
7.1
7.2
7.3
The consequence for Power Press Process.
The Consequence for Tapping Process.
The Consequence for Clinching Process.
106
106
107
7.4
7.5
The consequence for Bending Process.
The consequence for CNC Machining Process.
108
109
xiii
LIST OF ABBREVIATIONS
ACO
–
Ant Colony Optimization
JSP
–
Job Shop Problem
Np-hard
–
Non-deterministic Polynomial Time Hard
TSP
–
Traveling Salesman Problem
–
xiv
LIST OF SYMBOLS
Cmax
–
Makespan / total completion time
m
–
Total of machine
n
–
Total of job
i
–
node i
j
–
node j
Mm
–
m Machine
Jn
–
n Job
Jj
–
Jobs at node j
Oij
–
Operation at node i and node j
Pij
–
Processing Time
s
–
Source Node
t
–
Sink Node
λ
–
Wavelength
α
–
Real positive parameters
β
–
Real positive parameters
ρ
–
Evaporation rate
γ
–
Decision variable, generates a sequence
|O|
–
Operations
ηij
–
Heuristic value associates with the common Cij
τij
–
Pheromone value at node i and j
dij
–
Heuristic distance between node i and j
G
–
A representative of a graph
Sije
–
Empty Solution
xv
Sg
–
Good solution
Lk
–
Tour length of the k-th ant
–
xvi
LIST OF APPENDICES
APPENDIX
TITLE
PAGE