According to the technical demand of hot-rolling production in steel plant, a production scheduling mathematical model was proposed with the aim of reducing the production cost and optimizing the product quality. The scheduling of reheating furnaces which was summed up as the Boolean satisfiability problem was involved in rolling scheduling optimization which was summed up as the multiple traveling salesman problem with uncertain traveling salesman number, and a two-stage genetic-tabu algorithm was designed to solve the problem. It was shown that, the model could fully meet the demand of hot-rolling production. Compared to the human-computer method, the results had better performance on high production and energy efficiency.

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The demand for new energy will continue to expand as the environment changes and fossil energy decreases. However, the instability of new energy has slowed down the development of new energy. The joint use of new energy and energy storage modules effectively solves the shortcomings of new energy. The article proposed a lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm. Firstly the life model based on the battery capacity [Formula: see text], charging current [Formula: see text], and discharge current [Formula: see text] is built. Secondly, the deep learning method is used to improve the step length and speed change of artificial fish-school algorithm. Finally, the simulation platform detects the optimized parameters [Formula: see text]. The simulation results show that optimized parameters can help extend the life of the energy storage module.