  Can we allow humans to pick among different, yet reasonably similar,
decisions? Are we able to construct optimization problems whose outcome are
sets of feasible, close-to-optimal decisions for human users to pick from,
instead of a single, hardly explainable, do-as-I-say ``optimal'' directive?
  In this paper, we explore two complementary ways to render optimization
problems stemming from cyber-physical applications flexible. In doing so, the
optimization outcome is a trade off between engineering best and flexibility
for the users to decide to do something slightly different. The first method is
based on robust optimization and convex reformulations. The second method is
stochastic and inspired from stochastic optimization with decision-dependent
distributions.
