To adapt to the flexibility characteristics of modern manufacturing enterprises and the dynamics of manufacturing subsystems, promote collaboration in manufacturing functions, and allocate production resources in a reasonable manner, a mathematical model of integrated process planning and scheduling (IPPS) problems was developed to optimize the global performance of manufacturing systems. To solve IPPS problems, a hierarchical multistrategy genetic algorithm was developed. To address the multidimensional flexibility of IPPS problems, a chromosome coding method was designed to include a scheduling layer, a process layer, a machine layer, and a logic layer. Multiple crossover operators and mutation operators with polytypic global or local optimization strategies were used during the genetic operation stage to expand the algorithm’s search dimension and maintain the population’s diversity, thereby addressing the problems of population evolution stagnation and premature convergence. The effectiveness of the algorithm was verified by benchmark testing in the example simulation process. The test data show that if the makespan is taken as the optimization target, the proposed genetic algorithm performs better in solving IPPS problems with high complexity. The use of multistrategy genetic operators and logic layer coding makes a significant contribution to the improved performance of the algorithm reported in this paper.

为了适应现代制造企业的灵活性特征和制造子系统的动态变化，促进制造部门之间的协作，并以合理的方式分配生产资源，开发了集成过程计划和调度（IPPS）问题的数学模型来优化制造系统的全球性能。为了解决IPPS问题，开发了一种分层多策略遗传算法。为了解决IPPS问题的多维灵活性，设计了一种染色体编码方法，其中包括调度层，处理层，机器层和逻辑层。在遗传操作阶段，使用具有多型全局或局部优化策略的多个交叉算子和变异算子来扩展算法的搜索范围并维持种群的多样性，从而解决种群进化停滞和过早收敛的问题。在示例仿真过程中，通过基准测试验证了该算法的有效性。测试数据表明，如果以制造期为最优化目标，则该遗传算法在解决IPPS问题上具有较高的复杂度。多策略遗传算子和逻辑层编码的使用对本文所报告算法的改进性能做出了重大贡献。从而解决人口发展停滞和过早趋同的问题。在示例仿真过程中，通过基准测试验证了该算法的有效性。测试数据表明，如果以制造跨度为最优化目标，则该遗传算法在解决IPPS问题上具有较高的复杂度。多策略遗传算子和逻辑层编码的使用对本文报道的算法的改进性能做出了重大贡献。从而解决人口发展停滞和过早趋同的问题。在示例仿真过程中，通过基准测试验证了该算法的有效性。测试数据表明，如果以制造期为最优化目标，则该遗传算法在解决IPPS问题上具有较高的复杂度。多策略遗传算子和逻辑层编码的使用对本文报道的算法的改进性能做出了重大贡献。