| Author: | Yang, Ying |
| Title: | Two studies on the application of large-scale decomposition algorithms in maritime management |
| Advisors: | Wang, Shuaian Hans (LMS) |
| Degree: | Ph.D. |
| Year: | 2026 |
| Department: | Department of Logistics and Maritime Studies |
| Pages: | xiii, 144 pages : color illustrations |
| Language: | English |
| Abstract: | In recent years, large-scale decomposition optimization algorithms have played an increasingly crucial role in maritime management, particularly in addressing complex and new challenges and enhancing the precision and effectiveness of decision-making. This thesis collectively demonstrates the efficacy of large-scale decomposition algorithms in the field of shipping management by facilitating optimal operational decisions, contributing to reduced operational costs, improved service quality, and increased competitiveness in the fast-evolving maritime industry. The first study addresses the drone scheduling problem in shore-to-ship delivery (DSP-SSD) to amid growing interest in the integration of drones into maritime logistics. We introduce a mixed-integer programming model with time discretization that incorporates drone-related constraints, moving targets, and the need for multiple drone trips. While commercial solvers can handle this model in small-scale scenarios, we propose a tailored branch-and-price-and-cut (BPC) algorithm for larger and more complex cases. This algorithm integrates a drone-specific backward labeling algorithm, cutting planes, and acceleration methods to boost its effectiveness. Experiments show that the BPC algorithm substantially outperforms the commercial solvers in terms of solution quality and computational efficiency and that the inclusion of acceleration strategies in the algorithm enhances its performance. We also provide detailed sensitivity analyses of critical parameters of the model, such as the time discretization parameter and the number of ships, to gain insights into how our approach could be applied in real-world DSP-SSD operations. The second study focuses on cruise fleet management, which demands innovative and effective management strategies due to its growing popularity and the diversification of consumer travel preferences. Our approach provides a cohesive framework for the cruise fleet deployment and itinerary schedule problem. We first propose an integer programming model based on a space-time network that captures the movement dynamics of cruises over a planning horizon. To solve this comprehensive model efficiently, we introduce a tailored Benders decomposition approach augmented by the simultaneous Magnanti-Wong method, where a valid and easily computed Magnanti-Wong bound is designed. We validate our approach using extensive numerical experiments on both simulation instances and a real case study. The results demonstrate the effectiveness of our integrated solving scheme and the practical applicability of our advanced decomposition method, marking a significant advancement in the field of cruise fleet management. |
| Rights: | All rights reserved |
| Access: | open access |
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