Optimization of Green Supply Chain Management Based on Improved MPA

Abstract

With the advancement of industrialization and urbanization in the global market, the contradiction between economic development and environmental protection is becoming increasingly prominent. In response to the optimization problem, this study constructs a green supply chain network problem model with green constraints. In the second half of the iteration of the ocean predator algorithm, Gaussian mutation is used to replace the original fish swarm aggregation device effect, proposing an improved ocean predator algorithm to solve the green supply chain network model. The results demonstrated that the designed algorithm performed greater than other algorithms on all four benchmark functions. Except for the mean value of 2.17×10-202 when solving function 1, the other mean and standard deviation were all 0. When solving the multi-modal benchmark test function, the proposed algorithm still had the fastest convergence speed and the difference was more obvious. In small-scale testing sets, the proposed algorithm could find the best solution for the test instance, resulting in a lower total cost of 139,832.97 yuan, 148,561.28 yuan, and 147,535.81 yuan, respectively. In three different scale test sets, the proposed algorithm had the fastest convergence speed and successfully converged to feasible solutions. The research results verified the algorithm performance and its good application effect in handling green supply chain network problems, which helps optimize it.