用AGLA算法求解一类以TFT为目标的模糊Flow Shop调度问题

针对一类加工时间不确定的以总流经时间(TFT)为目标的置换Flow Shop调度问题，应用模糊数学的方法表示加工时间的不确定性，提出了一种改进的智能算法——异步遗传局部搜索算法(AGLA)。该算法初始种群的一个解由构造型启发式算法产生，其他解随机产生；通过引入一个加强的变邻域搜索机制和一个简单的交叉算子，对种群执行异步进化操作(AE)；算法最后加入重启机制防止陷入局部极小。仿真实验结果验证了AGLA解决模糊Flow Shop问题的有效性。

An Asynchronous Genetic Local Search Algorithm with Total Flow Time Criterion for Fuzzy Flow Shop Scheduling Problem

With the objective of total flow time, the permutation flow shop scheduling problem is discussed in this paper. The uncertain processing time is described by fuzzy mathematics. Furthermore, an improved genetic algorithm, asynchronous genetic local search algorithm (AGLA), is presented. In AGLA, an individual in the initial population is generated by a constructive heuristic method, and the others are randomly yielded. And then, by an enhanced variable neighborhood search strategy and a crossover operator, the asynchronous evolution is executed for each pair of individuals. Besides, a restart strategy is employed to avoid the problem of local minimum. Finally, numerical simulation results show the effectiveness of the AGLA for fuzzy flow shop scheduling problem.