Short Term Financial Planning under Uncertainty
提出一个随机线性规划模型来解决企业短期财务规划问题，相比传统均值模型更真实地反映不确定性，且计算复杂度相近。实证表明忽略随机性会带来显著成本，但计算节省有限。
This paper presents a stochastic linear programming formulation of a firm's short term financial planning problem. This framework allows a more realistic representation of the uncertainties fundamental to this problem than previous models. In addition, using Wets's algorithm for linear simple recourse problems, this formulation has approximately the same computational complexity as the mean approximation (i.e., the deterministic program obtained by replacing all random elements by their means). Using this formulation we empirically investigate the effects of differing distributions and penalty costs. We conclude that even with symmetric penalty costs and distributions the mean model is significantly inferior to the stochastic linear programming formulation. Thus we are able to demonstrate that ignoring the stochastic components in linear programming formulations can be very costly without having significant computational savings.