Beyond Optimal Solutions for Real-World Problems

Combinatorial optimisation technology has come a long way. We now have mature high-level modelling languages in which to specify a model of the particular problem of interest [18, 7, 24, 6]; robust complete solvers in each major constraint paradigm, including Constraint Programming (CP) [1, 19], MaxSAT [5, 11], and Mixed Integer Programming (MIP) [2, 3]; effective incomplete search techniques that can easily be combined with complete solvers to speed up the search such as Large Neighbourhood Search [23]; and enough general knowledge about modelling techniques to understand the need for our models to incorporate components such as global constraints [25], symmetry constraints [8], and more. All this has significantly reduced the amount of knowledge required to apply this technology successfully to the many different combinatorial optimisation problems that permeate our society. And yet, not many organisations use such advanced optimisation technology; instead, they often rely on the solutions provided by problem-specific algorithms that are implemented in traditional imperative languages and lack any of the above advances. Further, while advanced optimisation technology is particularly suitable for the kind of complex human-in-the-loop decision-making problems that occur in critical sectors of our society, including health, transport, energy, disaster management, environment and finance, these decisions are often still made by people with little or no technological support. In this extended abstract I argue that to change this state of affairs, our research focus needs to change from improving the technology on its own, to improving it so that users can better trust, use, and maintain the optimisation systems that we develop with it. The rest of this extended abstract discusses my personal experiences and opinion on these three points.