Operations Research Practice Exam
Operations Research Practice Exam
About Operations Research Exam
The Operations Research Practice Exam evaluates your ability to model, analyse, and solve complex decision problems using quantitative methods. This exam measures your understanding of optimization, simulation, network models, decision analysis, and forecasting techniques. Passing demonstrates you can apply operations research tools to improve efficiency, reduce costs, and support strategic decisions in real‑world settings.
Who should take the Exam?
- Operations managers and analysts
- Industrial and systems engineers
- Supply chain and logistics professionals
- Data analysts and business consultants
- Project managers and planners
- Graduate students in engineering, business, or mathematics
Skills Required
- Comfort with algebra and basic calculus
- Ability to set up and solve equations
- Familiarity with spreadsheets or solver software
- Analytical thinking and attention to detail
Knowledge Gained
- How to formulate linear and integer programs
- Use of network models for routing and flow problems
- Application of simulation to stochastic systems
- Techniques for decision trees and game‑theoretic analysis
- Methods for queuing, inventory, and project‑scheduling models
- Forecasting demand using time‑series and regression
Course Outline
Domain 1 – Introduction to Operations Research
- History, scope, and phases of problem solving
- Role of OR in business and engineering
Domain 2 – Linear Programming
- Formulation of LP models
- Simplex method and duality
- Sensitivity analysis
Domain 3 – Integer and Combinatorial Optimization
- Formulating integer programs
- Branch‑and‑bound and cutting planes
- Common combinatorial problems (knapsack, assignment)
Domain 4 – Network Models
- Shortest path and minimum spanning tree
- Maximum flow and transshipment models
- Applications in logistics and communications
Domain 5 – Decision Analysis and Game Theory
- Decision trees and expected value
- Risk attitudes and utility theory
- Two‑person zero‑sum games and Nash equilibrium
Domain 6 – Simulation and Stochastic Models
- Monte Carlo simulation principles
- Random variate generation
- Use of simulation software to model uncertainty
Domain 7 – Queuing and Inventory Models
- Single and multi‑server queues
- Economic order quantity and reorder point
- Service‑level trade‑offs
Domain 8 – Forecasting and Advanced Topics
- Time‑series methods (moving average, exponential smoothing)
- Regression‑based forecasting
- Introduction to metaheuristics and nonlinear programming
