Optimization Problem for Decision-making Process in Conditions of Limited Data Availability

Abstract

The daily work of a business professional involves making series of decisions. A large number of articles apply a broad range of optimization methods in their decision-making (DM) and achieve great results. However, there are still large gaps to overcome before companies can optimize the data they gather. Besides, making data-driven decisions is often emphasized, but effectively managing uncertainty is equally crucial. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for DM under uncertainty, and outlines future research opportunities. The purpose of this study is to present a mathematical framework that is well-suited to the limited information available in real-life problems and captures the decision-maker's attitude toward uncertainty. The developed framework was duly tested in the context of a healthcare problem, and proper recommendations were suggested in the given case study. Finally, we discussed the steps involved in this DM approach, the benefits it can provide to managers, as well as some of its limitations.