For the hydraulic cylinder parts manufacturing shop scheduling problem (HCPMS), which integrates a parallel batch processor hybrid flow shop scheduling problem with the flexible job shop scheduling problem, this paper establishes a multi-objective scheduling model with makespan, total energy consumption, and total machine workload as the optimisation objectives, and proposes an improved multi-objective migrating birds optimisation (IMOMBO) algorithm to solve the problem. First, considering the characteristics of the combination of single-piece and batch processing in the workshop, a double-layer coding rule based on the operation and processing equipment is proposed, and the corresponding decoding rule is designed according to whether the workpiece requires quenching and tempering. Second, a multi-population co-evolution mechanism is developed to enhance the diversity of solutions by conducting different evolutionary strategies. Additionally, six neighborhood structures are introduced to perform local searches for the leader and follower birds, thereby improving the quality of the solutions. Finally, the effectiveness of the IMOMBO algorithm is demonstrated by comparing its results with those of four other algorithms through comparative experiments and a practical case.

中文翻译：

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基于改进多目标候鸟优化的液压缸加工与热处理联合调度优化方法

针对将并行批处理器混合流水车间调度问题与柔性作业车间调度问题相结合的液压缸零件制造车间调度问题（HCPMS），建立了包含完工时间、总能耗和总能耗的多目标调度模型。以机器工作负载为优化目标，提出一种改进的多目标候鸟优化（IMOMBO）算法来解决该问题。首先，考虑到车间单件与批量加工相结合的特点，提出了基于操作和加工设备的双层编码规则，并根据工件是否需要淬火和加工，设计了相应的解码规则。回火。其次，开发了多群体协同进化机制，通过执行不同的进化策略来增强解决方案的多样性。此外，引入了六个邻域结构来对领导者和跟随者鸟类进行本地搜索，从而提高了解决方案的质量。最后，通过对比实验和实际案例，将IMOMBO算法的结果与其他四种算法的结果进行比较，证明了IMOMBO算法的有效性。