| Author: | Yang, Xiao |
| Title: | Autonomous ship scheduling optimization |
| Advisors: | Gu, Weihua (EE) Wang, Shuaian Hans (LMS) |
| Degree: | Ph.D. |
| Year: | 2022 |
| Subject: | Scheduling -- Data processing Shipping -- Data processing Mathematical optimization Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electrical Engineering |
| Pages: | xi, 98 pages : color illustrations |
| Language: | English |
| Abstract: | The shipping industry has started to investigate and employ autonomous ships to overcome various problems that come along with the ever-increasing waterborne transport volumes, energy consumption, and seafarer shortage. This dissertation deals with two autonomous ship scheduling problems: an autonomous ship scheduling problem with a waterway bottleneck, and an autonomous vessel train scheduling problem in a hub-and-spoke network. For the first problem, we develop a novel schedule optimization model for autonomous vessels passing a waterway bottleneck. The autonomous vessels are controlled by a central planner who enforces the optimal schedules. The model minimizes the vessel bunker cost and delay penalty at destinations by incorporating the realistic, nonlinear relationship between bunker consumption and sailing speed. The nonlinear model is linearized via two approximations. The first one linearizes the bunker consumption function using a piecewise linear lower bound, while the second does so by discretizing the time. Numerical experiments show that the discrete-time approximation model produces better solutions with lower computational costs than the continuous-time, piecewise-linear approximation, especially for large-scale problems. Numerical case studies are conducted for a real-world waterway bottleneck, the Three Gorges Dam lock. Results reveal how the optimal cost components and autonomous vessels' schedules and delays are affected by key operating parameters, including the fuel prices, delay penalty rates, and the tightness of sailing time windows. Comparison against two simpler benchmark scheduling strategies (one with no vessel coordination and the other adopting a naïve coordination) manifests the sizeable benefit of optimal autonomous vessel scheduling. Before resolving the technological difficulties required for a full automation, autonomous vessel train is a promising transitional solution to autonomous ship operations. A vessel train features a conventional, manned leader ship that pilots several autonomous ships (the followers) from their origin ports to destination ports. Present autonomous ships are small-sized and thus suitable for serving as feeders in a regional hub-and-spoke waterway network. We develop novel models for jointly optimizing the autonomous vessel assignment to the vessel trains, and the sequence of ports of call and the schedule of each vessel train in a hub-and-spoke network. Two mixed-integer programming models are developed, one for the freight distribution problem and the other for the vessel backhaul problem. Solutions to these models capture the optimal tradeoff between the added detour and delay costs of vessel trains and the lower sailing cost of autonomous ships. Numerical case studies are carried out for a real-world short-sea shipping network around the Bohai Bay of China. Results reveal sizeable cost savings of vessel train operations as compared to using conventional ships only. Sensitivity analyses are performed to unveil how the benefit of vessel trains is affected by key operating factors, e.g., the numbers of conventional and autonomous ships, the ratio between their costs, the maximum vessel train length, and the network topology. This study can be viewed as a first step toward real implementations of the economically competitive and environmentally friendly autonomous freight ships via vessel trains. |
| Rights: | All rights reserved |
| Access: | open access |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/11821