实际的车间调度问题往往具有更高的复杂度，调度算法需要考虑更多的约束条件，因此增加了问题的求解难度。为解决柔性作业车间批量调度场景中不同批次、不同工序之间可以无序加工的难题，进而突破现有车间机器使用率低、同类型机器负载不均衡的难点，文中构建了一种面向部分工序无序加工的柔性作业车间等量分批调度模型。首先，基于广泛使用的快速非支配排序遗传算法（NSGA-Ⅱ），提出了一种融合批次、批量和工序排序信息的两段编码结构，采用优先级规则方法获得初始种群，并以最小化完工时间、机器负载均衡率、机器总负荷为优化目标，采用贪心算法求解模型最优值，进而动态构建不同批次的加工路径；然后，对优化目标函数进行排序，再逐步加入非支配排序过程，以解决多个优化目标函数之间难以同时优化的问题，提高求解效率；最后，以某印刷包装企业的木制品加工车间为例，面向现场作业信息，采用仿真手段实现调度过程。结果表明，与优先级调度规则相比，文中所提方法的完工时间平均缩短了6.6%、机器负载均衡方差平均减小了10.7%，文中所提方法的机器负载均衡方差比遗传算法平均减小了53.3%，从而验证了文中方法的可行性，且该方法可以满足印刷包装企业柔性作业车间的高性能调度需求。

Actual job-shop scheduling problems often exhibit high complexity, and the scheduling algorithm needs to consider more constraints, so it increases the difficulty of solving the problem. To address the challenge of out-of-order processing for different batches and processes in the flexible job shop’s batch scheduling scenario, it is necessary to overcome the issues related to low utilization rates of existing job shop machines and unbalanced workload distribution among machines of the same type. Therefore, this paper constructed a flexible job shop equal batch scheduling model that incorporates partially out-of-order execution of processes. Firstly, based on the widely adopted fast non-dominated sorting genetic algorithm (NSGA-Ⅱ), this paper introduced a novel two-stage coding structure that integrates batch information and process sorting information. The priority rule method was used to obtain the initial population, and with minimizing the completion time, machine load equilibrium rate and total machine load as the optimization goal, the greedy algorithm was used to obtain the optimal value of the model, and then the processing path of different batches was dynamically constructed.Then, the optimized objective functions were sorted, and the non-dominant sorting process was added step by step to solve the problem that multiple optimized objective functions are difficult to optimize at the same time and improve the solving efficiency. Finally, taking the wood products processing workshop of a printing and packaging enterprise as an example, the scheduling process was realized according to the field operation information. The results show that, compared with the priority scheduling rules, the completion time of the proposed method is shortened by 6.6%, the average machine load balancing variance is reduced by 10.7%, and the average of the proposed method is reduced by 53.3% compared with the genetic algorithm, thus verifying the feasibility of the method. This method can meet the high performance scheduling requirements of the flexible workshop of printing and packaging enterprises.