Solution Repair/Recovery in Uncertain Optimization Environment

TL;DR

This paper addresses the challenge of repairing or adjusting solutions in optimization problems under uncertainty, where re-optimization is limited, by proposing methods for local solution repair to maintain feasibility and near-optimality.

Contribution

It introduces a framework for solution repair in uncertain optimization environments, enabling effective adjustments without full re-optimization.

Findings

Proposes local repair methods for solutions under uncertainty

Demonstrates effectiveness in maintaining feasibility and near-optimality

Applicable to operation management problems like scheduling

Abstract

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.