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Abstract Aiming at the problems of slow convergence speed and low precision probability of multi-objective optimization of energy storage materials, a multi-objective optimization model of energy storage materials based on NSGA-II algorithm was proposed. The association rule set of storage materials in the joint supply chain operation performance management system is extracted, and the rough vector feature distribution set multi-objective optimization method is used to decompose and optimize the characteristics of storage materials in the joint supply chain operation performance management system. Using NSGA-II optimization analysis method, this paper summarizes the power storage materials under the joint supply chain operation performance management system, and summarizes three kinds of inventory control: periodic inventory, inventory coding, and computerized inventory. Combined with the positive regression learning method of organizational operational performance, the multi-objective optimization decision of electric storage materials under the joint supply chain operational performance management system is realized. The simulation results show that under the joint supply chain operation performance management system, the proposed method reaches the optimal convergence after 65 iterations, the convergence speed is fast, and the accuracy probability reaches 1.000 after 80 iterations, which solves the problems of slow convergence speed and low accuracy probability, and has a good scheduling ability of energy storage materials.
Hu et al. (Tue,) studied this question.