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本文为Ｆｌｏｗ－ｓｈｏｐ问题的求解一般地构造了一类随机方法─—模拟退火算法。基于６种不同的随机抽样方式，分析表明求解算法渐近收敛于全局最优解集且具有多项式计算复杂特性。以不同实例规模的ＵＩＳ、ＦＩＳ和ＮＩＳ的Ｍａｋｅｓｐａｎ最小Ｆｌｏｗ－ｓｈｏｐ排序问题为例，计算结果表明模拟退火求解Ｆｌｏｗ－ｓｈｏｐ排序问题是有效的．
In this paper, a class of stochastic method ─ -simulation annealing algorithm is generally constructed for the solution of Flow-shop problem. Based on six different random sampling methods, the analysis shows that the solving algorithm asymptotically converges to the global optimal solution set and has polynomial computational complexity. Taking the Makespan minimum Flow-shop scheduling problem of UIS, FIS and NIS with different sample sizes as an example, the results show that simulated annealing solves the Flow-shop scheduling problem effectively.