Map of Operations Research
A connected guide to optimization, uncertainty, simulation, analytics, and decision-making. Explore the regions of OR, follow a learning path, and find the software, datasets, and references practitioners actually use.
OR is the discipline of building analytical models to make better decisions when intuition runs out — turning real systems into variables, objectives, constraints, and algorithms.
The regions of OR
The field organizes naturally into six connected regions — foundations, an optimization core, stochastic systems, networks and games, classical applications, and the modern analytics-driven practice that ties it together.
Foundations of OR
The vocabulary of decisions: variables, objectives, constraints, uncertainty, and algorithms.
Optimization Core
Mathematical programming — the engine room of OR.
Uncertainty & Stochastic Systems
Modeling randomness, queues, simulation, and decisions under risk.
Networks, Games & Systems
Flows, strategy, sequencing, and structured combinatorial problems.
Operations Applications
Inventory, logistics, supply chains, and revenue — where OR meets industry.
Modern OR Practice
Analytics, ML, software, datasets, and the contemporary practice stack.
Why Operations Research matters
OR turns complexity into decisions. It makes tradeoffs explicit, scales scarce resources, and pairs predictive models with prescriptive action.
Decisions over predictions
Forecasts estimate the future; OR prescribes what to do about it.
Tradeoffs made explicit
Cost, service, risk, fairness, and resilience modeled rather than argued.
Scarce resources, scaled
Allocate people, vehicles, machines, energy, beds, capital, and compute.
Automation with accountability
Models expose assumptions, constraints, and objectives for review.
Industries everywhere
Logistics, healthcare, energy, finance, manufacturing, defense, sports.
Bridges to data science
Predict-then-optimize, decision-focused learning, and reinforcement learning.