To stay relevant, modellers must move beyond textbook formulations and embrace these new paradigms. The core principle remains: a model is a purposeful abstraction of reality. But how we build, instantiate, and interact with that model has changed dramatically. The heat is on — and those who master these new methodologies will define the next decade of decision-making science. Want to dive deeper into any of these hot topics? Start with the SPO+ paper by Elmachtoub & Grigas (2022), or explore the cvxpy-layer documentation for differentiable convex optimisation.
Introduction Mathematical programming — the art and science of optimizing a system subject to constraints — has long been a cornerstone of operations research, management science, engineering, and economics. Yet the methodology of modelling within mathematical programming is itself undergoing a renaissance. Driven by big data, artificial intelligence, cloud computing, and the demand for explainable decisions, what’s “hot” today in modelling methodology is a shift from static, closed-form formulations to adaptive, data-driven, and hybrid paradigms. modelling in mathematical programming methodol hot