Building a stochastic programming model from scratch: a harvesting management example
从历史数据出发，构建木材价格和需求边界的随机过程，生成不同情景树并比较求解方案，发现情景树对决策模型影响显著。
We analyse how to deal with the uncertainty before solving a stochastic optimization problem and we apply it to a forestry management problem. In particular, we start from historical data to build a stochastic process for wood prices and for bounds on its demand. Then, we generate scenario trees considering different numbers of scenarios and different scenario-generation methods, and we describe a procedure to compare the solutions obtained with each approach. Finally, we show that the scenario tree used to obtain a candidate solution has a considerable impact in our decision model.