随机信息部分已知的比值优化模型的割平面求解法

研究了随机信息部分已知的比值优化模型的计算问题.对原模型应用对偶理论等价转化,通过考察转化后的模型结构,提出割平面算法.与已有的方法相比较,等价转化后的模型的变量维数将大大减少.最后运用算例测试算法的有效性,结果表明该算法的计算速度较已有的方法要快很多.该方法计算简单,存储需求小,比较适于求解随机信息部分已知的比值优化模型中的大规模问题.

Research on Cutting Plane Algorithm for the Ratio Optimization Model under Uncertainty

In this paper,computational problem of the ratio optimization models under information uncertainty are studied.The original model can be converted to its equivalent by duality theory.Notice that,the model has a special structure after equivalene transformation.The variable,compared with the existing methods,are greatly reduced by introducing cutting plane algorithm.Finally,the paper listed the analysis,the results show that the algorithm is much quicker than the linear programming algorithm on computation speed.It has played a special role for solving large-scale problems thanks to its simplicity and its very low memory requirement.