Mixed-integer linear programming (MIP) is a well-established state-of-the-art technique for computer-aided optimization. However, companies observe an increasing danger of disruptions that prevent them from acting as planned. One reason is that input data is often assumed to be deterministic, but in reality, they are afflicted with uncertainties which cannot be adequately described in MIPs, often. This is the point where Q-MIPs are intended to support future modeling.

In 2007, Subramani coined the terms Quantified Linear Program and Quantified Integer Program and thus extended the traditional notions of Linear and Integer Programming [1]. The resulting quantified programs are both a strong modelling language and an intuitive input to next generation optimization software.

We extended this notion one step further and introduced a minimax objective [2]: