LAUSR

Due to the wide variety of production orders and complex processes in the lithium battery cell rolling shop, it is easy to cause conflict deadlock problems when multiple devices are operating at the same time, thus blocking the tasks and affecting productivity. In this paper, we build a task scheduling framework for the rolling shop by combining the production process of lithium battery cells, and establish a production scheduling model for the rolling shop by considering the scheduling rules of order splitting and order prioritization. An improved genetic algorithm is proposed to solve the model with the completion time and delay time as the optimization objectives. First, heuristic rules are combined with GLR initialization methods to generate initial populations to improve population quality. Secondly, a simulated annealing algorithm with an elite retention strategy is introduced to screen the new population, and a neighborhood exchange strategy based on order priority ranking is proposed to overcome the problem that the iterative process is prone to illegal solutions. Finally, the encoding method and mutation repair mechanism are improved, and adaptive crossover and mutation operations are used to improve the search capability of the algorithm. The experimental results show that the improved genetic algorithm is feasible and effective in solving the optimization problem of rolling operation shop scheduling, and the algorithm has certain application value in improving the efficiency of smart shop operation.