Apply now
Master'sData, AI, Simulation & Analytics Applications open

Master’s in Optimisation, Operations Research and Decision Engineering

Online master's programme of 1,900 hours in optimisation, operations research and decision-making: 12 modules covering modelling, linear, integer and non-linear programming, metaheuristics, constraint programming and scheduling, stochastic models, simulation, multi-criteria decision-making and prescriptive analytics, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
Virtual lab · try it before you enrol

Take the controls.

Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Formulate decision problems as optimisation models.

02

Solve linear, integer and non-linear models with solvers.

03

Design heuristics and metaheuristics for combinatorial problems.

04

Model stochastic systems using queues and simulation.

05

Apply multi-criteria decision-making methods.

06

Integrate prediction and optimisation into prescriptive systems.

07

Communicate quantitative recommendations to decision-makers.

Profile

Who it's for

  • Operations research analysts building optimisation models for planning and logistics.
  • Optimisation engineers working with MILP, constraint programming and metaheuristics.
  • Decision scientists combining prediction, simulation and optimisation.
  • Supply chain planners redesigning networks, inventories and routes.
  • Prescriptive analytics consultants delivering decision support tools to clients.
  • Operations engineers improving production schedules and staff rotas.
  • Engineering or science graduates aiming to move into logistics and transport.
Standards & tools

What you'll work with

  • ISO 31000 (risk management guidelines)
  • IEC 31010 (risk assessment techniques)
  • ISO 22400-2 (key performance indicators for manufacturing operations management)
  • ISO 28000 (security and resilience management systems for the supply chain)
  • ISO 50001 (energy management systems)
  • ISO 9001 (quality management systems)
  • ISO 3534-3 (statistics vocabulary: design of experiments)
  • Gurobi
  • Pyomo
  • Google OR-Tools
  • AnyLogic
  • Python with SciPy
Syllabus

12 modules · 1900 h

  1. 1.1The modelling process in operations research
  2. 1.2Decision variables, constraints and objectives
  3. 1.3Classification of optimisation problems
  4. 1.4Descriptive, predictive and prescriptive models
  5. 1.5Decision analysis with trees
  6. 1.6Expected utility theory
  7. 1.7Cognitive biases in decision-making
  8. 1.8From the model to the recommendation
  9. 1.9Ethics in prescriptive analytics
  10. 1.10Case study: formulating a planning problem
  1. 2.1Formulating linear models
  2. 2.2Geometry and the simplex method
  3. 2.3Duality and shadow prices
  4. 2.4Sensitivity analysis
  5. 2.5The interior point method
  6. 2.6Transportation and assignment problems
  7. 2.7Network flows and shortest paths
  8. 2.8Algebraic modelling languages
  9. 2.9Commercial and open-source solvers
  10. 2.10Lab: production plan with Pyomo and HiGHS
  1. 3.1Binary variables and logic in models
  2. 3.2Branch and bound
  3. 3.3Cutting planes and valid inequalities
  4. 3.4Strong and weak formulations
  5. 3.5Facility location problems
  6. 3.6Knapsack and bin packing problems
  7. 3.7Vehicle routing problems
  8. 3.8Machine scheduling
  9. 3.9Benders decomposition and column generation
  10. 3.10Practical: warehouse location with MILP
  1. 4.1Convexity and optimality conditions
  2. 4.2Karush-Kuhn-Tucker conditions
  3. 4.3Gradient and Newton methods
  4. 4.4Quadratic programming and mean-variance models
  5. 4.5Second-order cone optimisation
  6. 4.6Mixed-integer non-linear programming
  7. 4.7Optimisation with automatic differentiation
  8. 4.8Scaling and ill-conditioned problems
  9. 4.9Global optimisation of non-convex problems
  10. 4.10Lab: portfolio optimisation with quadratic programming
  1. 5.1Constructive heuristics and local search
  2. 5.2Simulated annealing and cooling schedules
  3. 5.3Tabu search and adaptive memory
  4. 5.4Genetic algorithms and crossover operators
  5. 5.5Particle swarm optimisation
  6. 5.6Ant colony optimisation
  7. 5.7Variable neighbourhood search
  8. 5.8Matheuristics combining MILP and heuristics
  9. 5.9Tuning and statistical comparison of algorithms
  10. 5.10Workshop: metaheuristic for vehicle routing with time windows
  1. 6.1Constraint satisfaction models
  2. 6.2Propagation and search
  3. 6.3Global constraints such as alldifferent and cumulative
  4. 6.4Resource-constrained scheduling
  5. 6.5Job shop scheduling
  6. 6.6Staff shift planning
  7. 6.7Google OR-Tools CP-SAT
  8. 6.8Comparison with integer programming
  9. 6.9Industrial applications of constraint programming
  10. 6.10Practical: shift planning with CP-SAT
  1. 7.1Poisson processes and Markov chains
  2. 7.2M/M/c queueing theory
  3. 7.3Queueing networks
  4. 7.4Markov decision processes
  5. 7.5Dynamic programming and the principle of optimality
  6. 7.6Stochastic inventory management
  7. 7.7Reliability and replacement
  8. 7.8Monte Carlo simulation
  9. 7.9Analysis of simulation output
  10. 7.10Lab: sizing a contact centre with queueing models
  1. 8.1Discrete-event simulation
  2. 8.2Agent-based modelling
  3. 8.3System dynamics
  4. 8.4Model validation and verification
  5. 8.5Design of simulation experiments
  6. 8.6Simulation-based optimisation
  7. 8.7What-if analysis with simulation models
  8. 8.8Simulation and digital twins
  9. 8.9Communicating scenarios
  10. 8.10Workshop: simulation of a logistics terminal
  1. 9.1Problems with conflicting objectives
  2. 9.2The Pareto front and dominance
  3. 9.3Weighted sum and epsilon-constraint methods
  4. 9.4The analytic hierarchy process
  5. 9.5ELECTRE and PROMETHEE outranking methods
  6. 9.6The TOPSIS method of proximity to the ideal solution
  7. 9.7Group decision-making
  8. 9.8Robustness analysis of decisions
  9. 9.9Multi-criteria decision-making in sustainability
  10. 9.10Case study: supplier selection with AHP
  1. 10.1Combining prediction and optimisation
  2. 10.2Optimisation with embedded machine learning models
  3. 10.3Integrated prediction and optimisation
  4. 10.4Reinforcement learning for sequential decisions
  5. 10.5Learned heuristics for combinatorial problems
  6. 10.6Real-time optimisation
  7. 10.7Interpretability of optimal solutions
  8. 10.8Decision support systems
  9. 10.9Deploying optimisation models
  10. 10.10Lab: pricing with forecasting and optimisation
  1. 11.1Supply chains and logistics networks
  2. 11.2Production planning
  3. 11.3Transport and routing
  4. 11.4Energy and economic dispatch
  5. 11.5Healthcare and resource allocation
  6. 11.6Finance and portfolios
  7. 11.7Telecommunications and networks
  8. 11.8The public sector and service location
  9. 11.9Operational risk and resilience
  10. 11.10Case study: redesigning a distribution network
  1. 12.1Identifying the decision problem
  2. 12.2Data collection and analysis
  3. 12.3Model formulation
  4. 12.4Solution methods and algorithms
  5. 12.5Sensitivity and scenario analysis
  6. 12.6Decision support tool
  7. 12.7Validation with decision-makers
  8. 12.8Conclusions and recommendations to the decision-maker
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Gurobi, Pyomo, Google OR-Tools, AnyLogic and Python with SciPy, in free, academic or evaluation versions. Real procedures are studied, such as distribution network optimisation, production and shift planning, and multi-criteria supplier selection. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 31000, IEC 31010, ISO 22400-2, ISO 28000, ISO 50001, ISO 9001, ISO 3534-3.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 31000, IEC 31010 and ISO 22400-2 applied to real cases, not as a theoretical annex.
Industry software:Gurobi, Pyomo, Google OR-Tools, AnyLogic and Python with SciPy, in free, academic or evaluation versions.
Master's Thesis with defence:a decision support project, from problem identification and model formulation to solution algorithms, scenario analysis and validation with decision-makers, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Frame the decision
Identify the decision problem and collect and analyse the data.
Formulate the model
Formulate the problem as a linear, integer or constraint programming model.
Solve and simulate
Solve it with Gurobi, OR-Tools or a metaheuristic and test it in simulation.
Build the decision tool
Run sensitivity and scenario analysis and build a decision support tool.
Master's Thesis
Validate the recommendations with decision-makers and present them, defended before a panel.
Career opportunities

Where this takes you

Operations research analystOptimisation engineerDecision scientistSupply chain plannerPrescriptive analytics consultantOperations engineer
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Gurobi, Pyomo, Google OR-Tools, AnyLogic and Python with SciPy, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

Keep exploring

Related programmes

See all in Data, AI, Simulation & Analytics
Master's9,970 $

Master’s in Digital Twins and Operations Analytics Engineering

Online master's programme of 1,900 hours in digital twin engineering and operations analytics. 12 modules covering digital twin architecture, data integration, physical, data-driven and hybrid modelling,…

1900 hOnlineData, AI, Simulation & Analytics
Master's9,970 $

Master’s in Multimodality Engineering

Online master's programme of 1,900 hours in multimodal AI engineering. 12 modules covering modality fusion and alignment, per-modality representations, transformer architectures, contrastive learning, multimodal language models,…

1900 hOnlineData, AI, Simulation & Analytics
Master's9,970 $

Master’s in Simulation, Co-Simulation and HPC Engineering

Online master's programme of 1,900 hours in simulation, co-simulation and HPC engineering. 12 modules covering numerical methods, physical system modelling, FMI-based co-simulation, multiphysics, HPC architectures, parallel…

1900 hOnlineData, AI, Simulation & Analytics
Master’s in Optimisation, Operations Research and Decision EngineeringEnrol
Related programmes

More in Data, AI, Simulation & Analytics

Master’s in Federated Learning & Privacy-Preserving ML EngineeringView programme →Master’s in AI Governance and Compliance EngineeringView programme →Master’s in Time Series, Signals and Anomaly Detection EngineeringView programme →Master’s in Multimodality EngineeringView programme →Master’s in Graph ML Engineering for Networks and SystemsView programme →Master’s in Reinforcement & Imitation Learning for Control EngineeringView programme →

All Data, AI, Simulation & Analytics programmes → · How to apply · Fees & financing · Scholarships

Scroll to Top
SEIUM – Institute of Advanced Engineering
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.