Computational Management Science

Schultz, Rudiger

doi: 10.1007/s10287-015-0233-2pmid: N/A

Rios, Ignacio; Wets, Roger; Woodruff, David

doi: 10.1007/s10287-015-0230-5pmid: N/A

Data for optimization problems often comes from (deterministic) forecasts, but it is naïve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate the distribution of forecast errors and use that to construct the scenarios. Although sampling from the distribution of errors comes immediately to mind, we propose instead to approximate rather than sample. Benchmark studies show that the method we propose works well.