A decision support tool for the Heavy Lift Shipping Industry

Abstract

Jumbo Maritime is a company specialized in handling and transporting substantial cargoes which did not fit into a container. To compete in the heavy lift shipping market, a well-balanced fleet composition is essential. In the current situation, the heavy lift cargoes are transported by a fleet consisting of ten vessels, having a varying crane lifting capacity from 3000*10^3 to 650 *10^3 kilogram (Class K, H, J and E). However, in the past five years, it has become apparent that the deployment of the vessels based on the heaviest item (between 75-100% of the capacity) has only been needed in a meagre five percent of all transportations. The installed crane capacity on the Jumbo vessels are an overcapacity compared to the demand of the market. During this research, a decision support tool is developed to improve the match between the demand from the market in the heavy lift cargo industry and the supply of the heavy lift shipping vessels. This will result in an improved fleet composition that maximizes profit. Subsequently, the tool has been examined to see whether it can henceforth be applied to the fleet composition to support Jumbo Maritime in its leading role of supplying heavy loads.The Maritime Fleet Size & Mix Problem model was used to find the optimal fleet composition. The goal of this model is to maximize profit, achieved from transport orders based on reduced cost of the vessels. Considering that the proceeds of a load depend on market demand, the transport data of the past five years has been used as an input parameter. A fleet is chosen by taking into consideration both the technical and financial aspects. The technical aspect is the maximum combined crane capacity installed on the vessels. The financial aspects of the vessels exist of capital costs, labour costs, fuel costs and maintenance costs. The result of this model will lead to an optimization in the distribution of the vessel.Subsequently, the vessels that are at the end of their life will be replaced in the near future. The Vessels classified under H and E will be replaced by a variety of the newer J-light classes. The decision about the composition of the new vessels in the Jumbo fleet should be supported by the optimization model. The new vessels will be used as input in the model, together with the prediction data of possible future market needs. These future predictions will consist of three scenarios: 1) the demand will stay the same as it has been for the past five years; 2) Demands will rise with 6% due to an increase in the oil and gas industry; 3) a 35% increase in the renewable energy related market. The result of the three scenarios will be a new fleet composition with a corresponding profit.An optimal fleet composition will be the result when the transport data of the past five years, together with the current fleet characteristics, are used as input for the Maritime Fleet Size & Mix Problem. The current fleet composition [2:4:2:2] is in the optimized situation as followed [1:2:1:8], whereby the theoretical gain increases from 108 million to 181 million. When the future scenarios one, two and three together with the new vessel types (K: J: J-900: J-800: J-700: J-600) are introduced, new optimum fleet compositions arise. The new optimized compositions are as follows: scenario one: [1:1:0:1:0:6] with a profit of 56 million, scenario two: [1:1:0:1:0:6] with a profit of 68 million and scenario three [1:1:0:1:0:6] with a profit of 58 million.The results of the Maritime Fleet Size & Mix Problem show that the profit can be optimized with an alternative fleet composition. The mathematical model determines the composition of the fleet by using the transport data from the past five years. The optimization model is subjected to a number of assumptions to approach the reality. By also using the Maritime Fleet Size & Mix Problem model with the introduction of the J-light and combining it with the three different future scenarios, the vessel type with a combined crane capacity of 600 tonnes appears to be the most efficient in the J-light class.