COST‐VOLUME‐PROFIT ANALYSIS IN STOCHASTIC PROGRAMMING MODELS
用数学规划方法处理贡献毛利不确定下的成本-产量-利润分析，提出了三种基于安全优先准则的概率机会约束模型，并构建了参数二次规划模型来生成效率前沿，帮助管理者在不确定性下做出决策。
Abstract This paper applies mathematical programming to cost‐volume‐profit (CVP) analysis under contribution margin uncertainty. Three CVP probabilistic chance‐constraint models based on various safety‐first criteria for decisions under uncertainty are presented and compared. It is shown that a break‐even segment of the mean‐standard deviation frontier is a set of optimal solutions for the proposed models. An operational parametric quadratic programming (QP) model is constructed, and the efficiency frontier is generated. The procedures for locating an optimal solution on the efficiency frontier are then presented. The recommended QP procedure offers both technical relief from the computational difficulties posed by the probabilistic constraints and a desired flexibility in generating and presenting the relevant information for decisions under uncertainty.