Operations Research is not a collection of disconnected techniques.
It is a layered discipline — built progressively from mathematical modeling to large-scale algorithmic systems.
This Study Path organizes the core knowledge required to move from foundational Linear Programming to advanced topics such as decomposition methods, metaheuristics, and computational complexity.
The structure below reflects the progression commonly found in top university curricula and classical textbooks in the field
This page is intended as:
A long-term reference for structured study.
A framework to guide deeper exploration.
A map of how the discipline evolves technically
Every OR journey begins with modeling.
At this stage, you learn to:
Define decision variables
Construct linear objective functions
Translate constraints into algebraic form
Interpret feasible regions geometrically
Solve small models graphically
This is where mathematical abstraction meets decision-making.
Introduction to Operations Research — Hillier & Lieberman (10th ed.)
Operations Research: An Introduction — Hamdy Taha (10th ed.)