基于改进分布估计算法的带并行机模糊混合Flow Shop调度

针对处理时间不确定情况下带并行机的混合Flow Shop调度问题，基于模糊规划理论，采用一种模糊数排序的方法建立了调度模型；以最小化加权模糊最大完工时间的平均值和不确定度作为调度目标，提出一种改进分布估计算法(IEDA)求解上述问题。 IEDA算法采用基于NEH(Nawaz Enscore Ham)和破坏重建策略的初始化方法，对较优个体进行变邻域局部搜索以提高算法的局部搜索能力，同时采用破坏重建策略增加种群多样性，在最优解连续若干代没有改进时对其进行基于破坏重建策略的变邻域局部搜索，增强算法跳出局部最优的能力，并用正交设计的方法调节算法参数。仿真实验结果验证了本文算法的优越性。

An Improved Estimation of Distribution Algorithm Based Fuzzy Hybrid Flow Shop Scheduling with Parallel Machines

Aiming at the scheduling problem of fuzzy hybrid flowshop with parallel machines, this paper proposes an improved distribution estimation algorithm (IEDA). In the proposed algorithm, the method of ranking fuzzy numbers is used to establish the scheduling model, and the minimization of the weighted average and uncertainty of the fuzzy makespan is taken as the objective of scheduling. An initial population is generated by means of the NEH(Nawaz Enscore Ham) heuristic and the strategy of destruction and construction. The variable neighborhood searching is incorporated to enhance the local exploitation, and the strategy of destruction and construction is applied to improve the diversity of population. Moreover, when the best solution has no improvement for successive generations, the variable neighborhood searching will be adopted so as to escape from local optimum. In addition, an orthogonal experiment design is utilized to adjust the parameters of IEDA. The simulation results indicate the superiority of the proposed IEDA.