Multi Level Inventory Optimization Model for Power Materials Based on Multi Population Parallel Genetic Algorithm

In order to optimize the inventory cost of power materials and ensure the balance of inventory structure, based on the application of multi population parallel genetic algorithm, a multi-level inventory optimization model for materials was designed and studied using a certain power enterprise as an example. Calculate the total cost of multi-level inventory in the power supply chain based on the cumulative relationship of supply chain levels; based on the calculation results, establish an optimization model with the goal of minimizing the annual total cost; introducing a multi subpopulation collaborative evolution mechanism for genetic algorithm optimization, using the optimized algorithm to solve the model, output the optimal solution, and complete the optimization of multi-level inventory management of power materials. The comparative experimental results show that the designed method performs the best in multi-level inventory optimization of power materials.