Study of multiobjective speed optimization and multivariate analysis considering the carbon intensity indicator (CII) regulation

Abstract

Low-carbon shipping plays a pivotal role in ship operations, and this study is dedicated to optimizing economic benefits and environmental friendliness concurrently while adhering to carbon intensity indicator (CII) regulation. The study proposes a comprehensive flowchart incorporating multiobjective optimization and multivariate analysis, aimed at maximizing economic benefits and minimizing fuel consumption, with the CII regulation serving as a key constraint. By leveraging the Multi-objective Bonobo optimizer (MOBO), the study identifies the Pareto set to determine the final compromise solution using the multi-attribute decision-making (MADM) approach, TOPSIS, coupled with entropy weighting. The study employs four multivariate analysis (MVA) methods - self-organizing mapping (SOM), hierarchical clustering analysis (HCA), principal component analysis (PCA), and Student t-test - to gain in-depth insights into the decision variable space and corresponding performance space. The efficacy and versatility of the proposed flowchart are demonstrated through the analysis of two oil carriers, showcasing the impact of CII regulations and operational parameters on economic benefits and fuel consumption. Upgrading rank B to A will lead to a 3.50 % and 1.64 % reduction in total distance traveled at full load, while degrading to rank C will result in a 5.74 % and 6.93 % improvement in the distance for these two ships. This study underscores the ability to achieve trade-offs within the same CII rank and unveils valuable relationships between speeds, proportions, and overall performance through MVA. Ultimately, the study offers solid support for ship operations in compliance with CII regulations.