Towards a sustainable and resilient fleet management

While petroleum is still a major energy source, the vast majority of world trade is carried by the sea shipping industry. To reduce greenhouse gas emissions, it is urgent for both industry and government to promote green shipping. Considering the surges in fuel prices and concerns on environmental i...

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Bibliographic Details
Main Author: Tong, Yanyan
Other Authors: Mao Jianfeng
Format: Theses and Dissertations
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/69830
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Institution: Nanyang Technological University
Language: English
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Summary:While petroleum is still a major energy source, the vast majority of world trade is carried by the sea shipping industry. To reduce greenhouse gas emissions, it is urgent for both industry and government to promote green shipping. Considering the surges in fuel prices and concerns on environmental issues in recent years, researches should be done to move shipping towards a more environmentally and financially sustainable future. Unlike most of previous work where fleet deployment and bunkering management are considered separately, we develop an integrated fleet deployment and bunker management system in this work. The fleet deployment refers to the decisions on which ships to operate on which route, at what speed and in what cargos to be transported. The bunker management refers to the decision on which ships to bunker on which port and at what volume. Numerical results show that they should not be decomposed as current practices. A more realistic fuel consumption function that considers not only cruising speed but also freight tonnage onboard is adopted in the proposed models, which can further improve its effectiveness in practice. In this thesis, we explore on four such integrated models to cope with different business modes covering feeder service, industry shipping and tramp services. The Mixed Integer Programming models with nonlinear constraints are formulated. We have also extended the model from deterministic problem to stochastic version, where uncertain disruptions are taken into consideration during fleet planning. This work can be extended in the future to study the disruption management in tramp services, and to deal the positioning and loading sequence of cargos.