D'Math University | Finance & Actuarial

MSc Operations Research

A mathematically rigorous postgraduate programme in the science of optimal decision-making. Covering linear and integer programming, network optimisation, simulation, and stochastic modelling, graduates are equipped to solve the most complex resource allocation and planning problems faced by industry, logistics, and government.

Postgraduate 1 Year Online & Blended Decision Science
12
Core Modules
£60k
Average Graduate Salary
50+
Partner Universities
1
Year Programme

Programme Overview

Programme Overview

  • Comprehensive coverage of deterministic and stochastic operations research methods
  • Linear programming, duality theory, and sensitivity analysis in Semester 1
  • Integer programming, combinatorial optimisation, and network flows in Semester 2
  • Stochastic modelling, queuing theory, and Markov decision processes
  • Simulation methods using Arena, Python, and CPLEX/Gurobi solvers
  • Dissertation or applied consultancy project in an industrial setting
  • Strong emphasis on real-world application alongside theoretical rigour

Entry Requirements

  • BSc in Mathematics, Statistics, Engineering, or Computer Science (2:1 or above)
  • Strong background in calculus, linear algebra, and basic probability
  • Programming experience preferred (Python, MATLAB, or R)
  • Prior exposure to optimisation or decision mathematics advantageous
  • Professional experience in logistics, supply chain, or finance considered
  • Two academic or professional references
  • English proficiency: IELTS 6.5+ or equivalent

Core Curriculum

📐
Linear Programming
Simplex method, duality theory, sensitivity analysis, transportation and assignment problems.
🔢
Integer Programming
Branch-and-bound, cutting planes, Lagrangian relaxation, and heuristic methods for combinatorial problems.
🕸️
Network Optimisation
Shortest path, maximum flow, minimum spanning tree, and travelling salesman problem algorithms.
🎲
Stochastic Modelling
Markov chains, Poisson processes, queuing theory, and inventory models under uncertainty.
💻
Simulation Methods
Monte Carlo simulation, discrete-event simulation, variance reduction techniques, and Arena/Python implementation.
🤖
Metaheuristics & AI
Genetic algorithms, simulated annealing, tabu search, and reinforcement learning for complex optimisation.
📦
Supply Chain Analytics
Demand forecasting, inventory optimisation, logistics network design, and humanitarian operations research.
📋
Dissertation / Industry Project
Original applied research or a consultancy engagement with an industry partner solving a real OR problem.

Course Catalogue

Click any course to view its objective and learning outcomes.

ORS 501 Linear Programming +

Objective

To formulate and solve linear optimisation problems.

Learning Outcomes

  • Apply simplex method.
  • Use revised simplex.
  • Apply duality theory.
  • Use sensitivity analysis.
  • Solve large LPs.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Simplex Method on 2D LP
A small 2-variable LP is shown with its feasible polygon. Press Step to walk along vertices increasing the objective. Highlights the current vertex.
ORS 502 Integer Programming +

Objective

To solve discrete optimisation problems.

Learning Outcomes

  • Apply branch-and-bound.
  • Use cutting planes.
  • Apply Lagrangian relaxation.
  • Use heuristics for large problems.
  • Solve TSP and similar problems.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Simplex Method on 2D LP
A small 2-variable LP is shown with its feasible polygon. Press Step to walk along vertices increasing the objective. Highlights the current vertex.
ORS 503 Network Optimisation +

Objective

To model and solve network flow problems.

Learning Outcomes

  • Apply shortest path algorithms.
  • Use max flow / min cut.
  • Apply minimum cost flow.
  • Use assignment algorithms.
  • Solve transportation problems.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
ORS 504 Queueing Theory +

Objective

To model service systems with random arrivals.

Learning Outcomes

  • Apply M/M/1 and M/M/c queues.
  • Use Erlang formulas.
  • Apply queueing networks.
  • Use Jackson networks.
  • Apply queues to service design.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
ORS 505 Stochastic Programming +

Objective

To optimise under uncertainty.

Learning Outcomes

  • Apply two-stage stochastic programs.
  • Use scenario generation.
  • Apply Benders decomposition.
  • Use chance-constrained programming.
  • Apply robust optimisation.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
ORS 506 Inventory & Supply Chain +

Objective

To optimise inventory and supply chain decisions.

Learning Outcomes

  • Apply EOQ and periodic-review models.
  • Use multi-echelon inventory.
  • Apply safety stock formulas.
  • Solve facility location problems.
  • Use vehicle routing.
ORS 507 Simulation +

Objective

To build and analyse discrete-event simulations.

Learning Outcomes

  • Build discrete-event simulations.
  • Apply input modelling.
  • Use output analysis.
  • Apply variance reduction.
  • Validate simulations.
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
ORS 508 Game Theory & Decision Theory +

Objective

To analyse strategic and rational decision-making.

Learning Outcomes

  • Compute Nash equilibria.
  • Apply utility theory.
  • Use Bayesian decision theory.
  • Apply mechanism design.
  • Use auctions theory.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
ORS 509 Convex Optimisation +

Objective

To solve convex optimisation problems efficiently.

Learning Outcomes

  • Apply convex analysis.
  • Use interior-point methods.
  • Apply duality.
  • Solve SDPs.
  • Use disciplined convex programming.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
Interactive Activity — Gradient Descent on a 2D Loss Surface
Click anywhere on the surface to drop a starting point. Animation traces the descent path on the chosen loss function. Adjust the learning rate to see how step size affects convergence.
Loss: η = 0.10
Click on the loss surface to drop a starting point.
ORS 510 Combinatorial Optimisation +

Objective

To solve combinatorial problems with polyhedral methods.

Learning Outcomes

  • Apply matroid theory.
  • Use polyhedral methods.
  • Apply approximation algorithms.
  • Use heuristics and metaheuristics.
  • Solve graph problems.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
ORS 511 OR Software & Modelling +

Objective

To use industrial OR software for modelling.

Learning Outcomes

  • Use AMPL/GAMS for modelling.
  • Apply Gurobi or CPLEX solvers.
  • Build OR pipelines in Python.
  • Visualise OR results.
  • Document models for stakeholders.
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
ORS 512 Master's Project +

Objective

To complete an original OR research project.

Learning Outcomes

  • Identify a real OR problem.
  • Apply rigorous OR methods.
  • Use real industrial data.
  • Write a research-quality dissertation.
  • Present to OR practitioners.

Career Pathways

🚛
Supply Chain Analyst
Optimise inventory levels, logistics networks, and distribution systems for manufacturing and retail companies.
✈️
Airline/Transport Optimiser
Solve fleet assignment, crew scheduling, and revenue management problems for transport operators worldwide.
💰
Financial Optimisation Analyst
Apply portfolio optimisation, risk-adjusted asset allocation, and algorithmic strategies in investment management.
🏥
Healthcare OR Analyst
Improve hospital scheduling, patient flow, resource allocation, and emergency response planning.
🏛️
Government Policy Analyst
Apply decision science to public sector planning, infrastructure investment, and resource allocation policy.
🔬
OR Research Scientist
Advance the theoretical and applied frontiers of operations research at academic institutions or national labs.

Top Global Universities

MIT (Sloan) London School of Economics Imperial College London Cornell University Carnegie Mellon University University of Warwick ETH Zürich Georgia Tech University of Edinburgh NUS Singapore

Why D'Math University

STEP 01
Mathematical Rigour
Our curriculum is built on the full mathematical foundations of OR — duality proofs, convergence guarantees, and theoretical correctness alongside practical application.
STEP 02
Industry Software
Students work with Gurobi, CPLEX, Arena, and Python OR libraries — the exact tools used by consulting firms, airlines, and logistics companies.
STEP 03
Applied Projects
The dissertation or industry consultancy project gives graduates a portfolio piece demonstrating real-world OR impact for employers.
STEP 04
Expert Faculty
Learn from OR specialists with active research and consulting experience across supply chain, finance, healthcare, and transportation.
Enrol in MSc Operations Research →

Applications open year-round — join the next cohort today.