Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | en_US |
| dc.contributor.advisor | Wang, Shuaian Hans (LMS) | en_US |
| dc.creator | Wu, Yiwei | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13027 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Large-scale optimization for shipping operations management | en_US |
| dcterms.abstract | Shipping plays an important role in the global economy and international trade. It is not only the core logistics link connecting markets around the world, but also the cornerstone of driving global economic growth, ensuring the efficient allocation of resources, and promoting the comprehensive development of society. In this context, large-scale optimization in the field of shipping operations management is particularly important. Such optimization is not only crucial for improving transportation efficiency and reducing costs, but also directly affects important indicators such as customer satisfaction and service quality maintenance. Through large-scale optimization, such as precise scheduling, fleet deployment, and cargo management, shipping can maintain competitive advantages in a fiercely competitive market environment, while also promoting the sustainable development and environmental protection. In general, shipping is not only a key component of the global economy, but its internal large-scale optimization is also an important driving force for the advancement of the industry. This thesis investigates three important issues in large-scale optimization for shipping operations management, where the first one relates to the decarbonization of shipping, the second one relates to fleet repositioning for uncertain demand, and the third one relates to government ship scheduling with the consideration of health impacts. | en_US |
| dcterms.abstract | The first study introduces a joint optimization problem of speed optimization, voyage planning, and fleet deployment considering the impacts of displacement and sailing speed on fuel consumption. The problem is highly motivated by the global warming. To limit carbon dioxide emissions released by the shipping industry, the Energy Efficiency Operational Index (EEOI), a carbon intensity indicator, is widely adopted to assess each ship’s energy efficiency and guide the shipping operations management. Specifically, this study formulates a nonlinear mixed-integer programming (MIP) model which minimizes both the weekly cost and the average EEOI value of all deployed ships. To solve this nonlinear MIP model, a tailored exact algorithm is designed. The numerical results show that the instances with at most seven ship routes can be solved by the proposed algorithm within four minutes. The second study investigates a fleet deployment problem involving demand fulfillment, cargo allocation, fleet repositioning, and ship chartering with the consideration of multi-period periods, heterogeneous ships, and uncertain shipping demand, which is motivated by the huge uncertainty in the shipping market brought by the COVID-19 pandemic. To address this problem, this study uses multistage stochastic programming to formulate a linear MIP model and develops a Benders-based branch-and-cut algorithm. Numerical results indicate that compared to two-stage stochastic programming, multistage stochastic programming can help to obtain better solutions. Particularly, 90% of the benefit of the multistage model is due to better demand fulfillment as well as cargo allocation decisions, while 10% of the benefit is due to improved fleet deployment decisions. The first two studies focus on commercial ships, whereas the last study shifts its attention to government ships. This shift is attributed to the current stringent regulations on air emissions from ships, highlighting the need for the government to lead by example through meticulous scheduling of its government ships. Specifically, the third study focuses on a routing, scheduling, and speed optimization problem of government ships that account for the health effects of air pollutant emissions under different weather conditions. To this end, this study proposes a trip-based formulation and a set-covering formulation for the problem, and designs a branch-and-price-and-cut algorithm to effectively solve the problem. Efficiency of the proposed algorithm for computational instances is verified. | en_US |
| dcterms.extent | ix, 125 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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