智能排班问题的概率约束规划模型与有效求解

由于其广泛的应用领域与难解性,对不同形式智能排班问题的研究现已成为运筹学界的一个研究热点。为克服现有模型无法恰当反映人力需求约束实际特征与求解算法效率低等不足,本文对综合排班问题构建了两种不同形式的概率约束随机规划模型。利用样本平均近似方法和"大M方法",我们将概率约束问题转化为大规模整数线性规划问题,并综合运用了Lagrange松弛法、分解算法与信赖域算法等方法的思想,设计出了一有效的嵌套分解算法。最后,通过数值算例说明了本文模型的合理性与所设计算法的有效性。

Chance Constrained Programming Models for Intelligent Scheduling Problems and Their Effcient Solution

Due to their extensive application areas and solution diffculty,different forms of intelli-gent scheduling problems have now become a research hot in operations research.The current models can not properly reffect practical characteristics of labor requirement constraints and the relevant al-gorithms lack necessary effciency.To overcome these shortcomings,two forms of chance constrained programming models are proposed for comprehensive scheduling problems to ?exibly describe the labor requirement constraint...