The previous studies on the flexible job shop scheduling problems (FJSP) with machine flexibility and worker flexibility normally assume that each machine is operated by one worker at any time. However, it is not accurate in many cases because many workers may be required for machines in processing complex operations. Hence, this paper studies a universal version, i.e., FJSP with worker cooperation flexibility (FJSPWC), which defines that each machine can be used only if their required workers are prepared. A mixed-integer linear programming model tuned by CPLEX is established for the problem aiming to collaboratively minimize the makespan, maximum workload of machines and maximum workload of workers. To solve the problem efficiently, a Pareto-based two-stage evolutionary algorithm (PTEA) is proposed. In the PTEA, a well-tailored initialization operator and the NSGA-II structure are designed for global exploration in the first stage, and a competitive objective-based local search operator is developed to improve its local search ability and accelerate the convergence in the second stage. Extensive experiments based on fifty-eight newly formulated benchmarks are carried out to validate the effectiveness of the well-designed initialization operator and two-stage architecture. Comprehensive experiments are performed to evaluate the proposed PTEA, and the results reveal that the PTEA is superior to four comparison algorithms concerning the distribution, convergence, and overall performance.

以往关于具有机器灵活性和工人灵活性的柔性作业车间调度问题（FJSP）的研究通常假设每台机器在任何时候都由一名工人操作。但是，在很多情况下它并不准确，因为机器在处理复杂的操作时可能需要很多工人。因此，本文研究了一个通用版本，即具有工人合作灵活性的 FJSP（FJSPWC），它定义了每台机器只有在准备好所需的工人时才能使用。针对该问题建立了 CPLEX 调整的混合整数线性规划模型，旨在协同最小化完工时间、机器的最大工作量和工人的最大工作量。为了有效地解决该问题，提出了一种基于帕累托的两阶段进化算法（PTEA）。在PTEA中，第一阶段为全局探索设计了精心定制的初始化算子和NSGA-II结构，第二阶段开发了基于竞争目标的局部搜索算子以提高其局部搜索能力并加速收敛。基于 58 个新制定的基准进行了大量实验，以验证精心设计的初始化运算符和两阶段架构的有效性。进行了综合实验来评估所提出的 PTEA，结果表明 PTEA 在分布、收敛和整体性能方面优于四种比较算法。并开发了基于竞争目标的局部搜索算子，以提高其局部搜索能力并加速第二阶段的收敛。基于 58 个新制定的基准进行了大量实验，以验证精心设计的初始化运算符和两阶段架构的有效性。进行了综合实验来评估所提出的 PTEA，结果表明 PTEA 在分布、收敛和整体性能方面优于四种比较算法。并开发了基于竞争目标的局部搜索算子，以提高其局部搜索能力并加速第二阶段的收敛。基于 58 个新制定的基准进行了大量实验，以验证精心设计的初始化运算符和两阶段架构的有效性。进行了综合实验来评估所提出的 PTEA，结果表明 PTEA 在分布、收敛和整体性能方面优于四种比较算法。