Optimization in handling tasks in monorail transportation system at Busan port

9 31 33 35 37 - - - - - - - -
3 1 39 41 43 45 47 49 51 - - - - - - -
2 40 42 44 46 48 50 - - - - - - - -
4 1 52 54 56 58 60 62 64 66 68 70 72 74 76 78
2 53 55 57 59 61 63 65 67 69 71 73 75 77 -
5 1 79 81 83 85 87 89 91 93 95 97 99 101 103 -
2 80 82 84 86 88 90 92 94 96 98 100 102 104 -
6 1 105 107 109 111 113 115 117 119 121 123 125 - - -
2 106 108 110 112 114 116 118 120 122 124 - - - -
7 1 126 128 130 132 134 136 138 140 142 144 - - - -
2 127 129 131 133 135 137 139 141 143 145 - - - -
8 1 146 148 150 152 154 156 158 160 162 164 - - - -
2 147 149 151 153 155 157 159 161 163 165 - - - -
9 1 166 168 170 172 174 176 178 180 182 - - - - -
2 167 169 171 173 175 177 179 181 - - - - - -
10 1 183 185 187 189 191 193 195 197 199 - - - - -
2 184 186 188 190 192 194 196 198 - - - - - -
11 1 200 202 204 206 208 210 212 - - - - - - -
2 201 203 205 207 209 211 213 - - - - - - -
12 1 214 216 218 220 222 224 226 - - - - - - -
2 215 217 219 221 223 225 - - - - - - - -
13 1 227 229 231 233 235 237 239 241 - - - - - -
2 228 230 232 234 236 238 240 - - - - - - -
14 1 242 244 246 248 250 252 254 - - - - - - -
2 243 245 247 249 251 253 - - - - - - - -
• All containers have standard length of 20 feet
(one TEU).
• Loader will release containers if and only if there
is a shuttle ready at the handling position.
• All input parameters in the simulation are given
by the project manager.
The initial interface looks like as shown in Figure 7,
where all necessary inputs need to be filled before
further computation. User can choose type of input
(from an Excel file or manually set), decide the time
parameters such as time at change stations, loading-
unloading time, arriving time and leaving time (time
for shuttles coming in and out of the terminal), decide
the velocity for shuttles and trucks, as well as number
of loaders and shuttles at each terminal. Finally, user
can set the speed of animation to boost up the dis-
play speed. All parameters will then be saved with the
“Setup Parameter” button.
Next, the computation progress starts after clicking
the “Process” button. The task scheduling and task as-
763
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
Table 3: Result after scheduling with Greedy Algorithm
Ter Loader Task ID
1 1 1 3 5 13 15 7 17 9 19 11 21 23 25 -
2 2 4 6 12 14 16 8 18 10 20 22 24 - -
2 1 26 28 32 34 30 36 38 - - - - - - -
2 27 31 29 33 35 37 - - - - - - - -
3 1 41 39 43 45 47 49 51 - - - - - - -
2 40 42 44 46 48 50 - - - - - - - -
4 1 56 58 54 60 62 52 64 66 70 68 72 74 76 78
2 57 59 55 61 63 53 65 67 69 71 73 75 77 -
5 1 87 89 85 83 93 81 91 79 95 97 99 101 103 -
2 88 90 86 84 94 82 92 80 96 98 100 102 104 -
6 1 111 115 109 113 117 107 119 105 121 123 125 - - -
2 112 110 114 108 116 118 106 120 122 124 - - - -
7 1 132 134 136 126 130 138 128 140 142 144 - - - -
2 135 131 133 137 127 129 139 141 143 145 - - - -
8 1 146 148 150 156 158 160 154 152 162 164 - - - -
2 147 149 155 157 159 161 151 153 163 165 - - - -
9 1 174 166 176 178 168 180 172 170 182 - - - - -
2 175 167 177 179 169 173 171 181 - - - - - -
10 1 191 193 195 183 197 185 189 199 187 - - - - -
2 192 194 184 196 186 190 188 198 - - - - - -
11 1 208 210 200 212 202 206 204 - - - - - - -
2 211 209 201 213 207 203 205 - - - - - - -
12 1 224 222 226 214 216 220 218 - - - - - - -
2 223 221 225 215 217 219 - - - - - - - -
13 1 241 239 237 235 227 229 233 231 - - - - - -
2 240 238 236 234 228 230 232 - - - - - - -
14 1 254 252 250 242 244 248 246 - - - - - - -
2 253 251 249 243 245 247 - - - - - - - -
signing process will be done based on the overall lay-
out (as shown in Figure 5) and the given allocation
of shuttles from the input step. When finished, user
can view the result with the buttons below. In this pa-
per, since we mainly focus on the overall efficiency
of the system, we will only display the efficiency of
two transportation system (traditional and new sys-
tem) to make the comparison, as illustrated in Fig-
ure 8. From the chart, it is clear that new transporta-
tion system has increased the efficiency remarkably,
expressed through the reduced time taken in handling
all given tasks. The monorail system also satisfies the
threshold defined by the project manager.
Moreover, significant evaluation points of the system,
such as the total working time, total moving distance
of all shuttles and delay of tasks are also recorded as
shown in Table 4 and Table 5. Those tables present
the records after 10 trials as shown in Figure 7, where
Table 4 demonstrates results with random schedule
(unarranged schedule) and Table 5 shows results in
764
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
Table 4: Result with unarranged schedule
Simulation
Time
Average
Working
Time
Total
Working
Time
Average
Moving
Distance
Total Moving
Distance
Minimum
Delay
Maximum
Delay
Average
Delay
1075.50 s 392.92 s 97443.25 s 8857.17 m 2.20E+06 m 0.00 s 207.00 s 12.05 s
1259.25 s 388.35 s 96309.75 s 8751.22 m 2.17E+06 m 0.00 s 203.25 s 7.58 s
1123.75 s 391.59 s 97114.50 s 8824.36 m 2.19E+06 m 0.00 s 207.25 s 10.75 s
1266.50 s 390.99 s 96965.50 s 8808.42 m 2.18E+06 m 0.00 s 207.25 s 10.16 s
1068.00 s 391.48 s 97086.75 s 8822.76 m 2.19E+06 m 0.00 s 236.75 s 10.64 s
1167.00 s 391.21 s 97020.00 s 8816.62 m 2.19E+06 m 0.00 s 303.75 s 10.38 s
1253.25 s 392.57 s 97357.50 s 8849.10 m 2.19E+06 m 0.00 s 244.00 s 11.71 s
1174.50 s 393.96 s 97702.00 s 8878.35 m 2.20E+06 m 0.00 s 256.50 s 13.06 s
1107.00 s 392.27 s 97282.00 s 8843.17 m 2.19E+06 m 0.00 s 256.50 s 11.41 s
1260.75 s 389.39 s 96568.75 s 8774.63 m 2.18E+06 m 0.00 s 203.25 s 8.60 s
Average Value
1175.55 s 391.47 s 97085.00 s 8822.58 m 2.19E+06 m 0.00 s 232.55 s 10.63 s
Table 5: Result with arranged schedule using Greedy Algorithm
Simulation
Time
Average
Working
Time
Total
Working
Time
AverageMov-
ing Distance
Total Moving
Distance
Minimum
Delay
Maximum
Delay
Average
Delay
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
Average Value
1161.00 s 383.41 s 95086.00 s 8638.78 m 2.14E+06 m 0.00 s 203.25 s 2.76 s
765
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
Figure 6: Flowchart for task assigning procedure
case of arranged schedule (applying task scheduling).
Final results from Table 4 and Table 5 indicate that
arranged schedule reduces travel distance for about
2% and over 40 kilometres in total (46.88 kilometres).
The average delay also drops dramatically from 10.63s
to 2.76s (about 280%). These critical points reveal
huge advantages of a well-arranged schedule in logis-
tics activities because they can save large amount of
operational expenses and make remarkable benefits
for the managers.
ACHIEVED RESULTS
As shown in Figure 8, it is obviously that the new
monorail system has the finishing time much better
than traditional transportation system (using trucks)
within a same workload. The main reason is that
new system has lower loading time and gapping time
(time between each transfer), as well as higher mov-
ing speed. Basically, a truck can onlymove with speed
of 40-50 km/h and has to deal with traffic problems,
while a shuttle is manufactured to operate at 80-100
km/h without traffic congestion. Loading time for
shuttles is about 10 seconds with loader, while it takes
about 1 minutes to pick up a container for trucks. All
of those make the new system more efficient for in-
tense workload at Busan Port.
In addition, based on the simulation implemented in
Section 3, it is clear that the better schedule achieved
766
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
Figure 7: Interface’s appearance after filling inputs
Figure 8: Time taken when applying traditional and new transportation system
767
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
from Greedy Algorithm gives better result for the
whole process. Table 4 and Table 5 have shown that
all critical points (simulation time, total working time,
total moving distance, average moving distance, max-
imum delay and average delay) are improved a lot.
All time parameters are optimized as desired and they
will make great impact to the overall efficiency. Note
that the simulation is done based on a workload of
about 250 containers as a study case. In practice, the
real demand could be 100 times greater (about 25000
– 30000 containers per day). However, the optimiza-
tion principle still remains unchanged, and it is ex-
pected that the travel distance and the average delay
would also decrease for 2% and 280%, respectively
(according to Section 3).
DISCUSSION
According to the achieved results as in Section 4, it
is apparent that a well-organized plan for tasks will
grant more effectiveness in comparison to random-
ized plan. Nonetheless, in real world, a complete sys-
tem would comprise numerous parameters and crite-
ria to be considered, making the optimization prob-
lem more complicated and the solution sometimes
would not fit for all cases. Since this is a combinatorial
problem, there might be a lot of combination that can
lead to a same outcome, which means there has to be
a lot of time-taking computation, resulting in slow re-
sponse for real-time activities. For technical aspects,
it is reasonable to be approved with a non-global so-
lution, where only the dominant criteria are satisfied
and the trivial disadvantages can be ignored.
In our problem, the dominant criteria is chosen as the
total moving distance of working shuttles and the av-
erage delay for all tasks, and the solution found from
Greedy Algorithm supposes to fulfill the optimization
goal. However, currently it is not possible to prove the
uniqueness of the solution, and there might be other
better solutions for this problem. In this research, our
final goal is to reveal a good solution that can reduce
the total moving distance and the average delay, and
the achieved results from Section 4 seem to meet the
requirements.
CONCLUSION
To sum up, this research has proposed a solution for
optimization in scheduling and assigning in the new
ITT project being built at Busan Port in South Korea,
which are both essential in reducing operational cost.
Effectiveness of the optimization procedures has been
proved in our study case and it is expected to also en-
sure the precision in reality, where theworkload could
be thousands times greater.
In addition, it is emphasized that the obtained re-
sult mainly focuses on general behaviors of the new
ITT System in comparison with traditional transport
mode and does not give deeper analysis to the dy-
namic model of the system, where specification of de-
vices and equipment would be further evaluated such
as weight and dimension of shuttles and containers.
It is due to the fact that the project is still at the gen-
eral analysis stage, where all input parameters are not
fixed yet and the project managers are still consider-
ing. Realmodel for loaders and shuttles have not been
built yet, as well as the layout also has not been con-
structed completely, so dynamic properties of the sys-
tem are temporarily ignored. These factors are obvi-
ously important; however, they would be considered
at further analysis stages of the project.
At the moment, all parameters in the simulation are
proposed by the project managers, as the main ob-
jective of this stage is to evaluate the possibility of
the system. The simulation model is still not enough
constraints, such as velocity limits or load distribu-
tions. In later stages where the system is modelled
more completely, these constraints would be consid-
ered in order to fulfill the research.
ABBREVIATIONS
ITT: Inter-Terminal Transport
TEU: Twenty-feet Equivalent Unit
MATLAB: MAtrix LABoratory (a program from
MathWorks)
CONFLICT OF INTEREST
The authors wish to confirm that there are no know
conflicts of interest associated with this publication
and there has been no significant financial support for
this work that could have influenced its outcome.
AUTHORS’ CONTRIBUTION
All authors conceived of the study and participated in
its research and coordination and helped to draft the
manuscript. The authors read and approved the final
manuscript.
REFERENCES
1. Pieriegud J. Analysis of the potential of the development of
rail container transport market in Poland. Report of European
Commission. 2019;.
2. Davies P. Container Terminal Reservation Systems Design and
Performance. METRANS International Urban Freight Confer-
ence, Long Beach CA. 2013;.
3. Yang W, Song H. Railway Container Terminal Station Layout
and Operation Plan of Container Trucks. LISS 2014, Springer,
Berlin, Heidelberg. 2014;Available from: https://doi.org/10.
1007/978-3-662-43871-8_54.
768
Science & Technology Development Journal – Engineering and Technology, 4(1):758-770
4. Lee BK, et al. A Simulation Study for Designing a Rail Terminal
in aContainer Port. Proceedingsof the2006Winter Simulation
Conference, Monterey, USA, 2006;PMID: 16739990. Available
from: https://doi.org/10.1109/WSC.2006.323239.
5. Heilig L. Stefan Vob : Inter-terminal transportation: an an-
notated bibliography and research agenda. Flexible Services
andManufacturing Journal. 2017;29(1):35–63. Available from:
https://doi.org/10.1007/s10696-016-9237-7.
6. Rodrigue JP. Transportation Modes. in The Geography of
Transport Systems, 4th Edition, London: Taylor & Francis Ltd.
2017;.
7. Duinkerken MB, et al. Comparing transportation systems for
inter-terminal transport at the Maasvlakte container termi-
nals. OR Spectrum. 2006;28(4):469–493. Available from: https:
//doi.org/10.1007/s00291-006-0056-1.
8. World Shipping Council: Top 50 world container
ports;Available from: 
the-industry/global-trade/top-50-world-container-ports.
9. Negenborn R. Project: Innovative Concepts for Inter Termi-
nal Transport on Maasvlakte 1 and 2 at the Port of Rotter-
dam. TUDelft, Rotterdam. 2013;Available from: 
negenborn.net/rudy/projects_itt.html.
10. Duinkerken MB, Negenborn RR. Inter-terminal transport on
Maasvlakte 1 and 2 in 2030: Towards a multidisciplinary and
innovative approach on future inter-terminal transport op-
tions. TUDelft, Rotterdam. 2014;.
11. Heilig L, Voss S. Inter-terminal transportation: an annotated
bibliography and research agenda. Flexible Services andMan-
ufacturing Journal. 2017;29(1):35–63. Available from: https:
//doi.org/10.1007/s10696-016-9237-7.
12. Qiu L, et al. Scheduling and routing algorithms for
AGVs: A survey. International Journal of Production Research.
2002;40(3):745–760. Available from: https://doi.org/10.1080/
00207540110091712.
13. Ng WC. Crane scheduling in container yards with inter-
crane interference. European Journal ofOperational Research.
2005;164(1):64–78. Available from: https://doi.org/10.1016/j.
ejor.2003.11.025.
14. Hwan-Seong K. Busan Port New Port ITT Infrastructure
Project. Project document, KoreaMaritime andOceanUniver-
sity. 2019;.
15. Hoos HH, Stützle T. Travelling Salesman Problem. Stochastic
Local Search. 2005;.
16. Greco F. Travelling Salesman Problem. I-Tech Education and
Publishing KG, Vienna, Austria. 2008;Available from: https://
doi.org/10.5772/66.
17. Klarreich E. Computer Scientist Find New Shortcuts for
Infamous Travelling Salesman Problem. 2013;Available
from: https://www.wired.com/2013/01/traveling-salesman-
problem/.
18. Vince A. A framework for the greedy algorithm. Discrete Ap-
plied Mathematics. 2002;121:247–260. Available from: https:
//doi.org/10.1016/S0166-218X(01)00362-6.
19. Bang-Jensen J, et al. When the greedy algorithm fails. Discrete
Optimization. 2004;1(2):121–127. Available from: https://doi.
org/10.1016/j.disopt.2004.03.007.
20. Bendall G, Margot F. Greedy-type resistance of combinatorial
problems. Discrete Optimization. 2006;3(4):288–298. Avail-
able from: https://doi.org/10.1016/j.disopt.2006.03.001.
769
Tạp chí Phát triển Khoa học và Công nghệ – Kĩ thuật và Công nghệ, 4(1):758-770
Open Access Full Text Article Bài nghiên cứu
1Trường Đại học Bách khoa,
ĐHQG-HCM, Việt Nam
2Trường Đại học Hàng hải Hàn Quốc,
Busan, Hàn Quốc.
Liên hệ
Nguyễn Duy Anh, Trường Đại học Bách
khoa, ĐHQG-HCM, Việt Nam
Email: duyanhnguyen@hcmut.edu.vn
Lịch sử
 Ngày nhận: 08-06-2020
 Ngày chấp nhận: 22-03-2021 
 Ngày đăng: 31-03-2021
DOI : 10.32508/stdjet.v4i1.746 
Bản quyền
© ĐHQG Tp.HCM. Đây là bài báo công bố
mở được phát hành theo các điều khoản của
the Creative Commons Attribution 4.0
International license.
Tối ưu hóa trong giải quyết tác vụ trong hệ thống vận chuyển
monorail ở cảng Busan
Lê Ngọc Bảo Long1, Nguyễn Duy Anh1,*, Kim Hwan-Seong2
Use your smartphone to scan this
QR code and download this article
TÓM TẮT
Bài báo này trình bày một giải pháp tối ưu cho quá trình xử lý tác vụ ở cảng container Busan tại
Hàn Quốc. Ở thời điểm hiện tại, một hệ thống vận chuyển monorail đang được xây dựng và dự
kiến đưa vào sử dụng vào năm 2045. Đây là một dự án có sự tham gia của chính phủ Hàn Quốc
nhằmmục đích nâng cao hiệu suất sử dụng trong cảng Busan – một trong những cảng container
lớn nhất thế giới. Trong hệ thống này, phương tiện vận chuyển là các shuttle (hình dạng con thoi)
chạy dọc theo đường ray chỉ theo một chiều, thông qua các trạm đặc biệt gọi là trạm chuyển đổi
tương tự như hệ thống chuyển làn trên đường ray xe lửa để đến các trạm mong muốn nơi có các
thiết bị gắp đợi để xử lý. Toàn bộ quá trình hoạt động có thể được chia thành 2 khâu chính: lên kế
hoạch cho tác vụ và phân bổ tác vụ. Tất cả các container trước tiên phải được lên kế hoạch hợp lý
ở từng trạm dựa trên bảng tác vụ, và sau đó phải được phân bổ một cách tối ưu đến các shuttle
để đạt được kết quả tốt nhất. Bài báo chủ yếu tập trung vào bài toán tối ưu thời gian trong việc
lên kế hoạch và phân bổ tác vụ - những vấn đề tối ưu quan trọng nhằm mục đích giảm thời gian
vận hành và độ trễ trung bình của hệ thống. Để giải quyết vấn đề này, một giải thuật heuristic gọi
là giải thuật Tham Lam được tiến hành nhằm sắp xếp tác vụ được giao theo một trình tự hợp lý và
phân bổ những tác vụ đã sắp xếp cho shuttle phù hợp, bằng cách tính toán thời gian di chuyển
giữa các trạm và thời gian để shuttle tiếp cận loader. Tất cả kết quả quan trọng khi có và không có
giải thuật Tham Lam được ghi lại và so sánh để chỉ ra sự khác biệt giữa các quy trình, bao gồm tổng
thời gian hoạt động, tổng quãng đường di chuyển, quãng đường di chuyển trung bình và độ trễ
trung bình của toàn bộ quá trình. Tất cả mô phỏng được tiến hành trên phần mềm MATLAB, với
các số liệu đánh giá cuối cùng chỉ ra các tiêu chí quan trọng và làm nổi bật ưu điểm của hệ thống
mới, cũng như lợi ích của các phương pháp tối ưu đã áp dụng.
Từ khoá: Vận chuyển liên trạm, Hệ thống monorail, Lên kế hoạch, Phân bổ, Cảng Busan, Thuật
toán Tham lam
Trích dẫn bài báo này: Long L N B, Anh N D, Hwan-Seong K. Tối ưu hóa trong giải quyết tác vụ trong 
hệ thống vận chuyển monorail ở cảng Busan. Sci. Tech. Dev. J. - Eng. Tech.; 4(1):758-770.
770