In order to combat global climate change and reduce harmful emissions, various countries and International Maritime Organization (IMO) have issued and updated regulations that limit ship gas emissions. From a long-term perspective, more and more relevant regulations will be introduced for the development of green shipping. However, the uncertainty of policies poses a large challenge to ship design, especially early ship design. Without considering the uncertainty of policy, the ship design may have been excessive eco-friendly or under-eco-friendly in the long term, affecting the vessels in delivering value to key stakeholders throughout the lifecycle of the system.

This paper proposes a model to optimize the main design indicators of the ship to deal with the uncertainty associated with future environmental regulations and to ensure that the economic performance of the ship can still maintain market competitiveness. The optimization model is mainly composed of three parts, namely, the uncertainty prediction model of environmental regulation trends, the evaluation of the economic performance of the ship design and the modeling of the ship. Among them, the regulation uncertainty prediction model proposes a new ship emission indicator based on the current regulations, considering that the current regulations are facing unforeseen changes. The regulations trend simulation is loosely based on a Jump-diffusion process and then add bounds to create an uncertain environmental regulations scenario by using extrapolation. The proposed method is used to simulate the future regulation trends in a range to provide advice for designers when design freedom is still high. This framework optimizes the comprehensive economic performance of the ship that includes ship cost and total annual cost including the fuel cost of main engines, in-transit cargo inventory cost and non-fuel vessel operating cost etc.The early stage design of a container ship is used as a verification case to prove that the model can deal with the impact of regulation uncertainty effectively.

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