With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem.

考虑到分布式制造系统中的不确定性，本文针对具有模糊处理时间和模糊到期日的多目标模糊分布式混合流水车间调度问题进行了研究。为了同时优化模糊总时延和鲁棒性，通过合理地结合分布算法（EDA）估计和迭代贪婪（IG）搜索，提出了一种具有特定问题策略的协同协同进化算法。在EDA模式搜索中，建立了特定于问题的概率模型以减少求解空间，并提出了一种示例机制来生成新个体。为了增强利用，专门设计了本地搜索来提高非主导解决方案的性能。此外，IG模式搜索中的破坏和重建方法被用于进一步开发更好的解决方案。为了平衡勘探和开发能力，基于信息熵和精英解决方案的多样性，设计了一种模式切换协作方案。通过实验方法的田口设计，研究了关键参数对算法性能的影响。比较结果和统计分析证明了该算法在解决问题中的有效性。通过实验方法的田口设计，研究了关键参数对算法性能的影响。比较结果和统计分析证明了该算法在解决问题中的有效性。通过实验方法的田口设计，研究了关键参数对算法性能的影响。比较结果和统计分析证明了该算法在解决问题中的有效性。