Collaborative strategy of supply chain carbon emission reduction based on multi-objective optimization algorithm

Abstract

At present, the development mode of enterprises has changed from single to comprehensive, and a new trade mode has been formed in the carbon emissions trading, resulting in changes in the input and output of various enterprises. Therefore, it is inevitable to realize supply chain carbon emission reduction synergy. At present, the focus of China's supply chain carbon emission reduction research is to effectively reduce carbon emissions on the basis of synergy. Considering the diversity, autonomy and learning of supply chain node enterprises, it is suitable to use multi-objective optimization algorithm for modeling. Due to many excellent algorithms, multi-objective optimization algorithms have been widely used in solving problems. This paper proposed a co-evolutionary genetic algorithm (GA) and a multi-objective genetic algorithm (MOGA). Compared with traditional GA, MOGA has strong global search ability and has strong advantages in solving multi-objective optimization problems (MOOPs). The experimental findings in this article showed that the convergence time of GA and MOGA was 33.1 s and 18.8 s respectively when the number of iterations under the experimental sample was 1000. When the number of iterations under the test sample was 1000, the convergence time of GA and MOGA was 40.9 s and 21.7 s respectively. It can be found that the convergence time of MOGA is shorter than that of GA in both the experimental sample and the test sample, which indicates that it is faster and more efficient to find the supply chain carbon emission reduction synergy strategy.