Green maritime: An improved quantum genetic algorithm-based ship speed optimization method considering various emission reduction regulations and strategies

Abstract

Different organizations and countries have proposed a variety of policies and strategies to reduce emissions from the shipping industry. These include the Emission Control Area (ECA) policy, carbon tax policy, Vessel Speed Reduction Incentive Program (VSRIP), and Virtual Arrival (VA) strategy. These policies and strategies create additional costs for ships, forcing the ships to change their speeds or routes. This highlights the need to study approaches that optimize the sailing speeds of ships, by coordinating the implementation of the multiple governing policies and strategies. This paper develops a cost model that considers different emission reduction policies and strategies; this is followed by an analysis of their effect on emissions reduction. In addition, the paper proposes an improved quantum genetic algorithm based on a “fork gene” and elite strategy, which is specifically designed for specific shipping-related policies and strategy. The model is applied to a shipping route on the United States (US) West Coast, with three key findings. First, the improved algorithm significantly reduces the total cost, compared with the original alternative, in the context of emission reduction policies and strategies. Second, a single policy can produce positive results in reducing emissions, and the coordinated implementation of three separate policies (ECA, carbon tax, and VSRIP) may not lead to better results than a single policy. Finally, adding the VA strategy above and beyond the three other emission reduction policies may create more benefits in reducing emissions and operating costs.