Bounds and Approximations for Multistage Stochastic Programs
研究了多阶段随机规划问题的下界和上界获取方法，并通过多阶段库存问题展示其应用，帮助处理因场景树规模过大导致的计算困难。
Consider (typically large) multistage stochastic programs, which are defined on scenario trees as the basic data structure. It is well known that the computational complexity of the solution depends on the size of the tree, which itself increases typically exponentially fast with its height, i.e., the number of decision stages. For this reason approximations which replace the problem by a simpler one and allow bounding the optimal value are of great importance. In this paper we study several methods to obtain lower and upper bounds for multistage stochastic programs and we demonstrate their use in a multistage inventory problem.