Optimization of Supply Chain Scheduling Based on Genetic Particle Swarm Optimization Algorithm

In response to the optimization problem of large-scale customization supply chain scheduling, the author introduces a supply chain scheduling optimization approach that relies on a hybrid algorithm combining genetic algorithms and particle swarm optimization. The author first adopts mathematical programming modeling methods to formalize the problem into a mathematical model. In order to better apply intelligent search algorithms, some definitions are introduced to map the model to a directed graph structure for further analysis and solution. Secondly, a hybrid algorithm was developed by harnessing the global search capabilities of genetic algorithms and the rapid convergence capabilities of particle swarm optimization algorithms. This hybrid algorithm can fully leverage the advantages of these two algorithms when solving problems, making them complementary to each other and improving solution efficiency and quality. Finally, apply this hybrid algorithm to solve the supply chain scheduling optimization problem model. The results obtained by comparing the performance with other algorithms indicate that this hybrid algorithm performs better overall, providing a more effective solution for addressing the optimization problem of large-scale customization supply chain scheduling.