College of Information Science and Engineering, Henan University of Technology, China.
School of Art and Design, Changzhou Institute of Technology, China.
Math Biosci Eng. 2023 Jan 4;20(3):4838-4864. doi: 10.3934/mbe.2023224.
In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.
在当前的全球合作生产模式下，分布式模糊流水车间调度问题（DFFSP）受到了广泛关注，因为它考虑了实际流水车间调度问题中的不确定因素。本文研究了一种基于序列差分的差分进化多阶段混合进化算法（MSHEA-SDDE），用于最小化模糊完工时间和模糊总流动时间。MSHEA-SDDE 在不同阶段平衡算法的收敛性和分布性能。在第一阶段，混合采样策略使种群在多个方向上快速收敛到帕累托前沿（PF）。在第二阶段，使用基于序列差分的差分进化（SDDE）来加快收敛速度，以提高收敛性能。在最后阶段，改变 SDDE 的进化方向，引导个体搜索 PF 的局部区域，从而进一步提高收敛和分布性能。实验结果表明，MSHEA-SDDE 在解决 DFFSP 方面的性能优于经典的对比算法。
