批量及路径可变时机器ROBUST布置问题

应用混合遗传算法(HGA)解决了生产批量及路径可变时的车间ROBUST布置问题，即用遗传算法找到一种车间布置，它在各个生产时期都是最优或接近最优的．对遗传算法操作中产生的违反约束的个体采用修补和动态惩罚相结合的处理方法，即对变异操作产生的违反约束的个体采用修补的方法，同时在目标函数中引入惩罚函数控制种群中违反约束的个体数目，并设计了一个模糊控制器动态地调整惩罚系数，以解决遗传算法中的约束满足问题，提高了遗传算法的效率．仿真结果表明，所提出的动态调整惩罚系数的思想及方法是提高遗传算法效率的一种有效途径．

The Machine Robust Facility Layout Problem in the Dynamic and Flexible Production Environments

An HGA(hybrid genetic algorithm) was proposed to solve the facility robust layout problem in the dynamic and flexible production environments. In the algorithm a machine layout is found, which can get the optimal result in every period. The invalid individuals generated in the GA are handled by the repair method and penalty function method. That is, repair the invalid individual generated in the mute operation and introduce the penalty function to control the number of the invalid individual in the population, then a fuzzy controller is designed to adjust the value of penalty coefficient, which reflect the violated gene in individual, as a result the constraint satisfied problems in genetic algorithm are solved, and the searching efficiency of the genetic algorithm is improved. Finally, simulations were made, and the results show that it is a good way to improve the searching efficiency.