Designing control algorithms and simulating monorail system for inter terminal transport at busan port

VIETNAM NATIONAL UNIVERSITY – HO CHI MINH CITY HO CHI MINH UNIVERSITY OF TECHNOLOGY LE NGOC BAO LONG DESIGNING CONTROL ALGORITHMS AND SIMULATING MONORAIL SYSTEM FOR INTERTERMINAL TRANSPORT AT BUSAN PORT THIẾT KẾ GIẢI THUẬT ĐIỀU KHIỂN VÀ MÔ PHỎNG HỆ THỐNG MONORAIL TRONG VẬN CHUYỂN LIÊN TRẠM Ở CẢNG BUSAN Major: Mechatronics Engineering Major Code: 8520114 MASTER THESIS HO CHI MINH CITY, September 2020 i CƠNG TRÌNH ĐƯỢC HOÀN THÀNH TẠI TRƯỜNG ĐẠI HỌC BÁCH KHOA –ĐHQG -HCM Cán hướng dẫn khoa học : PGS.TS Nguyễn Duy Anh Cán chấm nhận xét : PGS.TS Nguyễn Thanh Phương Cán chấm nhận xét : TS Lê Ngọc Bích Luận văn thạc sĩ bảo vệ Trường Đại học Bách Khoa, ĐHQG Tp HCM ngày 03 tháng 09 năm 2020 Thành phần Hội đồng đánh giá luận văn thạc sĩ gồm: (Ghi rõ họ, tên, học hàm, học vị Hội đồng chấm bảo vệ luận văn thạc sĩ) PGS.TS Võ Tường Quân – Chủ tịch TS Lê Đức Hạnh - Ủy viên PGS.TS Nguyễn Thanh Phương – CBPB1 TS Lê Ngọc Bích – CBPB2 TS Trần Việt Hồng – Thư ký Xác nhận Chủ tịch Hội đồng đánh giá LV Trưởng Khoa quản lý chuyên ngành sau luận văn sửa chữa (nếu có) CHỦ TỊCH HỘI ĐỒNG TRƯỞNG KHOA CƠ KHÍ ii ĐẠI HỌC QUỐC GIA TP.HCM TRƯỜNG ĐẠI HỌC BÁCH KHOA CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM Độc lập - Tự - Hạnh phúc NHIỆM VỤ LUẬN VĂN THẠC SĨ Họ tên học viên: Lê Ngọc Bảo Long MSHV: 1970029 Ngày, tháng, năm sinh: 19/10/1995 Nơi sinh: Cần Thơ Chuyên ngành: Cơ Điện Tử Mã số : 8520114 I TÊN ĐỀ TÀI: Thiết kế giải thuật điều khiển mô hệ thống monorail vận chuyển liên trạm cảng Busan II NHIỆM VỤ VÀ NỘI DUNG: Định nghĩa phương thức vận chuyển (transport modes) hệ thống vận chuyển liên trạm (Inter-Terminal Transport System) Giới thiệu dự án ITT Monorail triển khai cảng Busan – Hàn Quốc Thiết kế giải thuật tối ưu việc xếp trình tự xuất hàng vị trí loader Thiết kế giải thuật tối ưu việc hoạch định đường trạm cảng Thiết kế giải thuật tối ưu điều khiển trình phân bổ tác vụ cho shuttle Viết phần mềm mô hoạt động hệ thống ITT Monorail cảng Busan dựa thông số đầu vào cung cấp, thực mô với 250 containers So sánh, đánh giá hiệu giải thuật thiết kế đề xuất phương án cải tiến III NGÀY GIAO NHIỆM VỤ : 24/02/2020 IV NGÀY HOÀN THÀNH NHIỆM VỤ : 15/08/2020 V CÁN BỘ HƯỚNG DẪN : PGS.TS Nguyễn Duy Anh Tp HCM, ngày 04 tháng 08 năm 2020 CÁN BỘ HƯỚNG DẪN (Họ tên chữ ký) CHỦ NHIỆM BỘ MÔN ĐÀO TẠO (Họ tên chữ ký) Nguyễn Duy Anh TRƯỞNG KHOA CƠ KHÍ (Họ tên chữ ký) iii ACKNOWLEDGEMENT First of all, I would like to express my gratefulness to Assoc Prof Nguyen Duy Anh and Prof Kim Hwan-Seong for their best inspiration and guidance to this thesis Professors helped me a lot to overcome difficulties in accomplishing the project with great knowledge and motivation Secondly, I would like to acknowledge the support of lecturers at Faculty of Mechatronic Engineering, Ho Chi Minh City University of Technology for the essential knowledge over the past six years, which has built up the platform for me to complete the thesis And finally, I also would like to thank my family and my friends, as well as my partners in the laboratory, who have always been by my side I just cannot think how I could be as I am now without your help Le Ngoc Bao Long iv ABSTRACT Busan Port is the largest container port in South Korea and 6th position on the world that handles nearly 80% of total national container cargoes and more than 21 million TEUs in 2018 Increasing transport demand year-by-year forces Korea Government to develop transport system at Busan Port to minimize the time taken and operational cost and improve throughput capability Referring other Inter-Terminal Transport systems (ITT Systems) around the world, in 2018, Korea Government decided to build an ITT Monorail system along Busan Port that links inner-terminals with a monorail This new transport system is expected to lower functional cost compared to traditional method, however the complexity of project requires careful analysis and optimization approaches to maximize the overall efficiency The main objectives of this thesis is to realize the optimization problems that system should overcome and design optimization algorithms to resolve, as well as to evaluate the effectiveness of designed approaches and propose solutions for better performance Result of thesis shows the optimal sequence for loaders handling, solves shortest-path-finding problem for shuttles and gives best assignment strategy for given tasks, as well as highlight the differences after optimizing v TÓM TẮT LUẬN VĂN Cảng Busan cảng container lớn Hàn Quốc đứng thứ giới, với khả xử lý gần 80% tổng lượng hàng container nước 21 triệu đơn vị TEU năm 2018 Nhu cầu vận chuyển tăng liên tục qua năm buộc phủ Hàn Quốc phải phát triển hệ thống vận chuyển cảng Busan để giảm thiểu thời gian chi phí vận hành, tăng suất xuất nhập hàng Sau tham khảo hệ thống vận chuyển liên trạm (ITT) giới, phủ Hàn Quốc định xây dựng hệ thống ITT Monorail dọc theo cảng Busan để liên kết trạm thành phần đường ray chiều Hệ thống kỳ vọng giúp giảm bớt chi phí vận hành so với phương pháp vận chuyển truyền thống (dùng xe tải), nhiên mức độ phức tạp dự án yêu cầu phân tích cẩn trọng giải pháp tối ưu để nâng cao hiệu suất Mục tiêu luận văn tìm hiểu tốn tối ưu mà hệ thống cần đạt thiết kế giải thuật điều khiển để giải chúng, đồng thời đánh giá tính hiệu giải thuật đề xuất phương án cải tiến Kết luận văn chu trình xuất hàng tối ưu cho loader, giải tốn tìm đường ngắn cho shuttle đưa chiến lược phân bổ nhiệm vụ tốt nhất, làm bật khác biệt kết hợp giải pháp tối ưu vi DECLARATION I pledge that this thesis “DESIGNING CONTROL ALGORITHMS AND SIMULATING MONORAIL SYSTEM FOR INTER-TERMINAL TRANSPORT AT BUSAN PORT” is conducted on my own, with supervision from Professor Duy Anh Nguyen and Professor Kim Hwan-Seong – who is head manager of the ITT Monorail Project in Busan Port, and has not been submitted to any other universities and institutes, or for any other personal purposes All data, formulas and materials used in this thesis are truthful and fully cited, as they were proposed, verified and permitted by Professor Kim Hwan-Seong Le Ngoc Bao Long vii TABLE OF CONTENT ACKNOWLEDGEMENT iv ABSTRACT v TÓM TẮT LUẬN VĂN vi DECLARATION vii TABLE OF CONTENT viii LIST OF FIGURES xi LIST OF TABLES xiii LIST OF ACRONYMS xiv Chapter 1: INTRODUCTION 1.1 Definition of Transport Modes 1.1.1 In-land transportation 1.1.2 Maritime transportation 1.1.3 Air transportation 1.1.4 Intermodal transportation 1.2 Inter-Terminal Transport System 1.3 Literature Review 1.3.1 ITT Systems in the world 1.3.2 Simulation and Optimization studies 12 1.4 ITT Project at Busan Port 13 1.4.1 Scenario 13 1.4.2 Overview of Busan Port 13 1.4.3 Stages and objectives of project 14 1.5 Thesis’s range and objectives 16 1.5.1 Author’s module in the project 16 viii 1.5.2 Thesis’s range: 16 1.5.3 Thesis’s objectives: 16 Chapter 2: METHODOLOGY 17 2.1 General structure of ITT Monorail System 17 2.1.1 Main concept 17 2.1.2 Operational structure 17 2.1.3 Change station’s behavior 19 2.1.4 Prototypes of Loader and Shuttle 20 2.1.5 Loading and unloading processes 20 2.2 Transport demand at Busan Port 21 2.3 Calculating facilities required for the system 21 2.3.1 Calculating Shuttle Quantities Required 21 2.3.2 Calculating Loader Quantities 24 2.4 Applying in study case 24 Chapter 3: DESIGNING ALGORITHMS 26 3.1 Dispatching algorithm for shuttles 26 3.1.1 A* Algorithm 27 3.1.2 Ant Colony Optimization 27 3.1.3 Comparison between two methods 30 3.2 Scheduling algorithm for loaders 32 3.3 Assigning algorithm for shuttles 34 3.3.1 Distance-based approach 34 3.3.2 Time-based approach 34 Chapter 4: SIMULATION RESULTS 38 4.1 Assumptions made 38 4.2 Graphic User Interface program 38 ix 4.3 Result charts and tables 40 4.3.1 Efficiency chart 40 4.3.2 Loader’s productivity chart 41 4.3.3 Container flow at terminals 41 4.3.4 Shuttle’s record chart 42 4.4 Comparison between approaches 42 4.4.1 Distance-based approach without task scheduling 43 4.4.2 Distance-based approach with task scheduling 44 4.4.3 Time-based approach without task scheduling 45 4.4.4 Time-based approach with task scheduling 46 4.5 Evaluation 48 Chapter 5: CONCLUSION 50 5.1 Achievements 50 5.2 Drawbacks 51 5.3 Future works 51 APPENDIX: PUBLICATION 53 APPENDIX: COMMENT LETTER FROM KMOU 54 REFERENCES 55 LÝ LỊCH TRÍCH NGANG 60 x Chapter 4: SIMULATION RESULTS 4.4.4 Time-based approach with task scheduling Table 4.4: Result of time-based approach with arranged schedule using ACO Algorithm (low Ant number) Simulation Average Working Total Working Average Moving Total Moving Average Time (s) Time (s) Time (s) Distance (m) Distance (m) Delay (s) 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2439.500 1439.284 115142.750 32268.623 2581489.813 153.354 2490.000 1430.841 114467.250 32078.563 2566285.070 152.389 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2490.000 1433.641 114691.250 32141.937 2571354.955 153.271 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2339.250 1443.938 115515.000 32373.117 2589849.335 154.874 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2490.000 1433.641 114691.250 32141.937 2571354.955 153.271 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2722.000 1437.172 114973.750 32221.503 2577720.276 153.669 2570758.000 151.833 Average value after 30 trials 2520.625 1433.341 114667.258 32134.475 46 Chapter 4: SIMULATION RESULTS Table 4.5: Result of time-based approach with arranged schedule using ACO Algorithm (high Ant number) Simulation Average Working Total Working Average Moving Total Moving Average Time (s) Time (s) Time (s) Distance (m) Distance (m) Delay (s) 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2362.000 1428.678 114294.250 32028.618 2562289.427 149.523 2562289.427 149.523 Average value after 30 trials 2362.000 1428.678 114294.250 32028.618 47 Chapter 4: SIMULATION RESULTS 4.5 Evaluation After collecting simulation results as described in the previous subsection, overall evaluations are made depending on the essential criteria as following:  In case of randomized schedule (referring to Table 4.1 and Table 4.3), distance-based and time-based approach are likely to give the same simulation time Distance-based approach gives the lower total working time (113266.100s compared to 113702.850s) and total travelled distance (2539123.867m compared to 2548930.886m), however time-based approach obtains better average delay (203.441s compared to 214.576s) In percentage, total working time and travelled distance of distance-based approach are approximately 0.386% lower, while average delay is 5.189% higher than of timebased approach The variation is due to the difference in cost function of two methods, in which one focuses on the distance from shuttles to tasks and one focuses on the current delay of tasks  For distance-based approach (referring to Table 4.1 and Table 4.2), the simulation time, working time, travelled distance and delay are 3.591%, 0.306%, 0.310% and 29.164% improved, which means 94s, 347s, 7.9km and 63s in total, respectively  For time-based approach (referring to Table 4.3, Table 4.4 and Table 4.5), in case of low Ant number (NA = 100), the simulation time and average delay are 3.162% and 25.368% decreased (83s and 52s in total, respectively), while working time and moving distance have an increase at about 0.85% (about 21.8km) Increasing the Ant number to NA = 300 results in better convergence and also obtains much better results, in which the simulation time and average delay drop 9.256% and 26.503% (241s and 54s in total), respectively, while working time and moving distance rise slightly about 0.52% (about 13.3km in total) Since we apply ACO Algorithm in time-based approach, the convergence rate is quite sensitive to the number of virtual ants Larger size of samples leads to better convergence but also more computational cost  In case of arranged schedule (referring to Table 4.2, Table 4.4 and Table 4.5), the comparative advantages of two methods are similar to randomized schedule’s case, where distance-based approach obtains lower working time and working distance and time-based approach improves the delay Low-NA time-based approach seems to share the same simulation time and delay with distance-based approach, while High-NA 48 Chapter 4: SIMULATION RESULTS time-based approach improves the simulation time and delay at 5.821% and 1.627% respectively, in comparison to distance-based approach  As a consequence, High-NA time-based approach with task scheduling would give the lowest delay and finishing time, while distance-based approach would minimize the workload for each shuttle It is dependent on specific situations that managers should consider to choose the relevant method that would best suit their desires and requirements  General evaluation for every method is visualized in Figure 4.9 as following: Figure 4.9: Evaluation between applied approaches 49 Chapter 5: CONCLUSION Chapter 5: 5.1 CONCLUSION Achievements As the ultimate goal for this thesis, simulation results in Section 4.5 and Figure 4.9 indicates that both Distance-based Scheduled, Time-based Scheduled Low-NA and Time-based Scheduled High-NA approaches could reduce operational cost but in different ways due to difference objective functions Distance-based Scheduled approach stresses on minimizing distance travelled of shuttles, hence reducing total travelled distance and working time (approximately 0.31%) Time-based Scheduled approach focuses on minimizing total working time by evaluating task delays but with trade of increasing moving distance (lowering 9.26% simulation time, improving 26.50% delay time but increasing 0.85% moving distance) Based on specific requirements and limitation, managers could decide which approach would be best However, achievements in this thesis are dependent on the assumptions made in Section 2.4 and Section 4.1, which are still not corresponding to reality’s conditions The simulation is conducted with only 254 containers input, which was scaled down significantly from real transport demand (about 30000-50000 containers daily) In this aspect, it is considered and approved by the project managers that the simulation mainly focuses on the general operation scene and comparative advantages of new ITT Monorail System to the traditional transport system, not the absolute and precise results Due to capability of author’s computer, it is not possible to run the simulation program with such inputs Instead, author has to send the algorithms to ask for KMOU’s assistance for verifying the results, where the facilities are qualified Up to now, all results of author are validated by the project managers of KMOU, with the verification letter attached in the Appendix To sum up, author has got the achievements described as the following:  Constructed the system’s operational structure  Calculated the facilities required for the system  Built up the dispatching algorithm for shuttles  Designed the scheduling algorithm at loader’s positions  Designed the assigning algorithm for shuttles based on the cost functions 50 Chapter 5: CONCLUSION  Designed a User-Graphic-Interface program on MATLAB that allows users to test the system’s operation with chosen inputs  5.2 Validated the accuracy and made comparisons between applied methods Drawbacks Beside those achievements, the thesis also comprises some drawbacks First, the simulation model is still missing some working conditions that can make the results differ from reality The movements of shuttles along the monorail need some more advanced constraints, such as deceleration when entering terminals and change stations It is a complicated issue in algorithm design, and to temporarily resolve, author decides to add an amount of deceleration time at both loading and unloading activities, which ensures the total cycle time would not be changed The optimum speed and the parking locations of shuttles are not defined yet, which will also affect the overall operation It is because the project is still at stage for flexible configuration, which all parameters are not fixed yet This project’s stage only concentrates on general operation scene and how shuttles move along the monorail to accomplish given tasks Stricter working conditions, such as energy consumption of the whole system, workload distribution of shuttles or accidentally malfunctions occurrence, would be considered in next stages where deeper analysis would be made Next, the classification of container types is not considered carefully In fact, the TEU ratio in the port is usually in range 1.3 – 1.6, which means there may be 40-feet containers among the 20-feet ones In reality, a shuttle is expected to carry TEUs at once, while the simulation model has assumed that all containers are one TEU and each shuttle only takes one container at once Finally, the buffer of CSs also contributes a lot to the system’s performance To minimize the traffic congestion, each CS has a buffer to hold some containers and navigate them when the buffer is full 5.3 Future works To deal with the drawbacks mentioned above, author manages to the following actions in future, to completely and consistently solve the problem:  Add more realistic constraints to the simulation model, in order to mimic the real system as much as possible  Adjust the TEU factor to a general value (usually in range 1.3 – 1.6)  Increase the capability of each shuttle (to TEU per batch) 51 Chapter 5: CONCLUSION  Add deceleration progresses to the shuttles, especially at the terminals (handling positions) and the change stations (two ends)  Improve the performance of the CSs  Figure out the best configurations for the system for best simulation results  Do researches about other optimization algorithms and verify each other 52 APPENDIX APPENDIX: PUBLICATION The following publications has been published or certificated as a condition for researchoriented student for master thesis’s defense Full papers are attached within as a part of this thesis International journals:  Long Le Ngoc Bao, Duy Anh Nguyen, Kim Hwan-Seong, “Applying A* algorithm in routing for Inter-Terminal Transport System at Busan Port”, Maritime Technology and Research, Vol 2, No 4, Accepted: 11/05/2020 E-ISSN: 2651-205X  Ngoc Cuong Truong, Hwan-Seong Kim, In-Yong Kim, Long Le Ngoc Bao, Duy Anh Nguyen, “Design and simulation of a monorail network for the inter-terminal transport”, Korean Institute of Navigation and Port Research, Vol 44, No 5, Certificated: 23/06/2020 ISSN for Electronics version: 2093-8470 53 APPENDIX APPENDIX: COMMENT LETTER FROM KMOU The following comment letter has been written by Professor KIM Hwan-Seong – head manager of the ITT Monorail Project that author has participated in This letter is considered as a verification for the truthfulness and preciseness of the simulation program as well as the applied algorithms that author proposed in this thesis 54 REFERENCES REFERENCES [1] J.-P Rodrigue, "Transportation Modes," in The Geography of Transport System, 5th ed., New York, Routledge, 2020 [2] V News, "Nearly 4,000 plastic waste containers clogged 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26, no 1, pp 29-41, 1996 59 LÝ LỊCH TRÍCH NGANG LÝ LỊCH TRÍCH NGANG Thơng Tin:  Họ tên: Lê Ngọc Bảo Long  Ngày tháng năm sinh: 19/10/1995  Nơi sinh: Cần Thơ  Địa liên lạc: Đường 59, Phường 10, Quận 6, Thành phố Hồ Chí Minh Q Trình Đào Tạo  Từ 08/2013 đến 04/2019: sinh viên Đại Học, chương trình Việt Pháp, khoa Cơ Khí, chuyên ngành Cơ Điện Tử, Đại học Bách Khoa Tp.HCM  Từ 06/2019 đến nay: học viên Cao Học, chuyên ngành Cơ Điện Tử, khoa Cơ Khí, Đại học Bách Khoa Tp.HCM 60 ... INTRODUCTION 1.2 Inter- Terminal Transport System Inter- Terminal Transport System (ITT System) [23] [24] [25] [26] [27] is a transport system that connects organizationally separated areas and terminals... hợp giải pháp tối ưu vi DECLARATION I pledge that this thesis ? ?DESIGNING CONTROL ALGORITHMS AND SIMULATING MONORAIL SYSTEM FOR INTER- TERMINAL TRANSPORT AT BUSAN PORT? ?? is conducted on my own,... of Transport Modes 1.1.1 In-land transportation 1.1.2 Maritime transportation 1.1.3 Air transportation 1.1.4 Intermodal transportation 1.2 Inter- Terminal