Cargo scheduling and ship routing : simultaneous evaluation of fuel costs, opportunity costs and port charges
Master thesis
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https://hdl.handle.net/11250/2772132Utgivelsesdato
2021Metadata
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- Master Thesis [4490]
Sammendrag
In this thesis we propose an approach to find cost minimizing schedules on how contracts of affreightment can be carried out.
For cost control our approach covers fuel costs and a set of vessel specific costs which are not related to fuel. Consumption of fuel is expressed as a function of distance sailed, sailing speed and the consumption properties of each specific vessel. For non-fuel related costs, we include opportunity costs and some port charges. Each vessel has an opportunity cost associated with its use and the fees charged at different ports may depend on the size and type of the vessel.
Our model is based on a network flow formulation of the capacitated heterogeneous vehicle routing problem with time windows and with pickup and delivery. Speed as a variable makes the model considerably more demanding in terms of solving time. For that reason, the problem is divided into two formulations. First: the model solves a main problem that finds the cost minimizing schedule using a fixed speed. Second: the model solves a speed optimization problem separately for each route used in the schedule. Compared to a model without this splitting, our model approach offers a reduced ability to find minimum cost schedules. However, it allows small instances of the problem to be solved within a reasonable amount of time while also including some way of adjusting speed.
Our aim is to model an approach that contributes to the development of a tool which can provide useful insights and improve schedulers’ ability to find cost effective schedules. The model’s solutions are not to be interpreted as complete schedules ready to be put into action unsupervised; The model is deterministic and no considerations of uncertainties are integrated. The purpose of the model’s output is to serve as a cost-efficient starting point for schedulers to further adjust and tailor for their risk preferences.