基于正态云模型的状态转移算法求解多目标柔性作业车间调度问题

为了求解具有多目标多约束的柔性作业车间调度问题,提出一种基于正态云模型的状态转移算法.构建以最小化最大完工时间、机器总负荷及瓶颈机器负荷为目标的多目标柔性作业车间调度问题的数学模型;针对灰熵关联度适应度分配策略在Pareto解比较序列与参考序列之间的差值相等时不能引导算法进化的情况,提出一种改进灰熵关联度的适应度值分配策略;同时引入兼具模糊性和随机性的云模型进化策略以改进状态转移算法,可有效避免算法早熟并增加候选解的多样性.仿真结果表明:基于正态云模型的状态转移算法能够有效解决多目标柔性作业车间调度问题;与其他算法相比,所提出算法求解问题的收敛精度更高、收敛速度更快.

State transition algorithm based on normal cloud model for solving multi-objective flexible job shop scheduling problem

In order to solve the flexible job shop scheduling problem with multi-objectives and multi-constraints, a state transition algorithm based on normal cloud models is proposed. A mathematical model of multi-objective flexible job shop scheduling problems with the goal of minimizing the maximum completion time, total workload and bottleneck machine workload is constructed, and an adaptive value allocation strategy to improve the grey entropy correlation degree is proposed, which can not guide the evolution of the algorithm when the difference between the Pareto solution comparison sequence and the reference sequence is equal using the fitness allocation strategy of grey entropy correlation degree. At the same time, the cloud model evolution strategy with both fuzziness and randomness is introduced to improve the state transition algorithm, which can effectively avoid the precocious of the algorithm and increase the diversity of candidate solutions. The simulation results show that the state transition algorithm based on normal cloud models can effectively solve the multi-objective flexible job shop scheduling problem, and compared with other algorithms, this algorithm has higher convergence accuracy and faster convergence speed.