A data-driven target-oriented robust optimization framework: bridging machine learning and optimization under uncertainty

The target-oriented robust optimization (TORO) approach converts the original objectives to system targets and instead maximizes an uncertainty budget or robustness index. Machine learning techniques are used to tighten uncertainty sets which also reduce the pessimism of robust solutions. However, existing approaches operate only on a one-way sequential flow, where the data estimation and optimization modules implement their tasks independently. This overlooks the potential of capturing feedback within the solution framework to update forecasts and decisions based on the recent realizations of the uncertainty and system outcomes. This research proposes a novel closed-loop data-driven TORO framework, leveraging on the power of machine learning and feedback systems for optimization under uncertainty. The framework provides an array of solutions for various risk appetites and supports efficient decision-making as computational tractability is retained. A hypothetical case study is solved on a combined inventory and routing problem to demonstrate its applicability and features.