Operations research

Operations research is based on mathematical modelling with links to statistical analysis, machine learning and optimisation techniques to solve complex decision-making problems. It focusses on improving decision-making processes and finding optimal solutions to various problems in business, engineering, healthcare, logistics, and other fields.

Key aspects of operations research include:

Mathematical Modelling: Operations research practitioners use mathematical equations, algorithms and models to represent real-world problems. These models can range from linear, nonlinear programming and integer programming to queuing theory and simulation.

Optimisation: Operations research aims to find the best possible solutions among a set of feasible options. Optimisation techniques are employed to maximise or minimise specific objectives, such as profit, cost, time or resource utilisation. Important theoretical themes include large-scale decomposition, multi-objective optimisation and advanced metaheuristics. Furthermore, an emphasis on optimisation connected to data-driven decision-making under uncertainty is also essential in generating important theoretical results and significant partnerships.

Decision Support: Operations research provides decision-makers with valuable insights and tools to make informed decisions. It helps analyse various scenarios, assess risks and identify the best course of action.