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.INDEX TERMS Lithium battery rolling mill, rolling shop, job shop scheduling, genetic algorithm, adaptive crossover-mutation.
Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value.
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