I'm working on solve an optimization model. Linear decision rule is also used. Generally, when 2nd-stage variable is adapted to more uncertainty data, the results are expected to be better(monotonically increasing or decreasing). However, based on my results, the results show up and down as I increase the number of uncertainty data in adaptation. Could anyone explain about it? Or where am I wrong? Thank you!
More uncertainty data adapted to 2nd-stage variable, the results are expected to be better. Like, monotonically increasing (max problem) or decreasing (min problem).