Operations research is a scientific discipline focused on modeling, analyzing, and optimizing decision-making processes in complex systems. It relies on rigorous mathematical, statistical, and algorithmic methods to solve problems related to resource allocation, planning, risk management, or logistics. Unlike artificial intelligence, which often leverages machine learning and unstructured data, operations research emphasizes deterministic, explicit problem-solving based on formal mathematical models. This approach yields optimal or near-optimal solutions with high explainability and robustness.
Use cases and examples
Operations research is widely used in industry, logistics, transportation, finance, healthcare, and supply chain management. For example, it optimizes delivery routes, inventory management, scheduling, or staff assignment. In the airline industry, it helps minimize fuel costs and improve operational efficiency. In healthcare, it organizes operating rooms and allocates hospital resources.
Main software tools, libraries, frameworks
Leading operations research tools include optimization solvers like CPLEX, Gurobi, and the open-source CBC. Libraries such as PuLP (Python), OR-Tools (Google), and SciPy (for numerical optimization) are commonly used. Modeling environments like AMPL, Pyomo, or JuMP (Julia) offer powerful ways to describe and solve complex problems.
Recent developments, evolutions, and trends
Recent trends include integrating operations research with artificial intelligence, notably for dynamic or uncertain problems. Machine learning is increasingly used to refine optimization models or accelerate solving. Open-source tools are gaining traction, and cloud computing enables tackling large-scale problems. Emphasis is also placed on the interpretability and robustness of solutions to meet regulatory and industrial requirements.