Optimization Models for Collaborative Vessel Allocation : A Computational Study of How Collaboration Between Shipping Companies Can Reduce Fuel Costs and CO2 Emissions
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- Master Thesis 
Transportation by sea entails costs for shipping companies as well as emissions that contributes to the challenges regarding global warming. A variety of approaches can be implemented in order to facilitate reductions of these measures. In our thesis, we study how collaboration between shipping companies that carries out a sequence of deliveries with time windows can be a way of reducing fuel costs and CO2 emissions. To explore this, we formulate two optimization models in terms of mixed integer linear problems that minimizes the fuel costs resulting from the sequence of deliveries. The main decisions to be made in these models are the vessel allocation and the choice of speed levels. Fuel consumption forms the basis for the fuel costs and the CO2 emissions. Because the relationship between speed and fuel consumption is nonlinear, the relationship is linearized to formulate linear models. Collaboration is defined in terms of a collaborative decision of vessel allocation and speed levels where the shipping companies join their fleets of vessels and the deliveries that are requested to be carried out. In our computational study, the models are implemented using a dataset obtained from the company Signal Ocean. In addition, data regarding fuel consumption is collected from the Clarksons Research Portal. A variety of time window scenarios are implemented in order to explore the effects of collaboration when the underlying assumptions changes. The results show that joining the fleets of vessels and the requested deliveries in the decision of vessel allocation and choice of speed levels implies considerable reductions in both fuel costs and CO2 emissions.