Solution Repair/Recovery in Uncertain Optimization Environment
  Operation management problems (such as Production Planning and Scheduling)
are represented and formulated as optimization models. The resolution of such
optimization models leads to solutions which have to be operated in an
organization. However, the conditions under which the optimal solution is
obtained rarely correspond exactly to the conditions under which the solution
will be operated in the organization.Therefore, in most practical contexts, the
computed optimal solution is not anymore optimal under the conditions in which
it is operated. Indeed, it can be "far from optimal" or even not feasible. For
different reasons, we hadn't the possibility to completely re-optimize the
existing solution or plan. As a consequence, it is necessary to look for
"repair solutions", i.e., solutions that have a good behavior with respect to
possible scenarios, or with respect to uncertainty of the parameters of the
model. To tackle the problem, the computed solution should be such that it is
possible to "repair" it through a local re-optimization guided by the user or
through a limited change aiming at minimizing the impact of taking into
consideration the scenarios.
