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Master’s in Optimisation and OR for Engineering

Online master's programme of 1,900 hours in optimisation and operations research for engineering: 12 modules covering linear and integer optimisation, design optimisation, process optimisation, industrial planning and scheduling, logistics, energy systems, infrastructure, metaheuristics, simulation-based optimisation and OR implementation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Formulate engineering problems as mathematical optimisation models.

02

Solve linear, integer and non-linear programming models.

03

Optimise the design of components and processes.

04

Plan and sequence industrial operations.

05

Apply optimisation to energy systems and infrastructure.

06

Combine simulation and optimisation in complex systems.

07

Implement operations research tools in organisations.

Profile

Who it's for

  • Optimisation engineers working on design, operation and planning problems.
  • Operations research analysts in industrial and infrastructure settings.
  • Process engineers optimising reactors, energy integration and plant operation.
  • Production planners responsible for scheduling and sequencing.
  • Energy systems engineers working on dispatch, grids and storage.
  • Operations consultants implementing planning tools for clients.
  • Engineering or science graduates aiming to move into the energy sector.
Standards & tools

What you'll work with

  • ISO 50001 (energy management systems)
  • ISO 55000 (asset management: overview, principles and terminology)
  • ISO 22400-2 (key performance indicators for manufacturing operations management)
  • IEC 62264-1 (enterprise-control system integration)
  • ISO 31000 (risk management guidelines)
  • ISO 9001 (quality management systems)
  • Gurobi
  • Pyomo
  • Google OR-Tools
  • IPOPT
  • Siemens Plant Simulation
Syllabus

12 modules · 1900 h

  1. 1.1Engineering problems as optimisation problems
  2. 1.2Design, operation and planning
  3. 1.3Formulating models with physical constraints
  4. 1.4Time scales and decision levels
  5. 1.5Modelling languages: Pyomo, JuMP and AMPL
  6. 1.6The solver ecosystem
  7. 1.7Model reproducibility
  8. 1.8Reference cases in engineering
  9. 1.9Input data quality
  10. 1.10Case study: formulating the design of a pipe network
  1. 2.1Flow models in physical networks
  2. 2.2Formulating logical constraints with big-M
  3. 2.3Linearising non-linearities
  4. 2.4Unit commitment models
  5. 2.5Equipment sizing with integer variables
  6. 2.6Location and capacity models
  7. 2.7Solver tuning and tolerances
  8. 2.8Symmetry and alternative formulations
  9. 2.9Decomposition for large models
  10. 2.10Lab: thermal unit commitment with MILP
  1. 3.1Parametric and shape optimisation
  2. 3.2Density-based SIMP topology optimisation
  3. 3.3Gradient methods with sensitivity analysis
  4. 3.4The adjoint method for design gradients
  5. 3.5Multidisciplinary optimisation of coupled systems
  6. 3.6Surrogate models and kriging
  7. 3.7Bayesian optimisation with acquisition functions
  8. 3.8Manufacturability constraints
  9. 3.9Coupling with finite elements
  10. 3.10Practical: topology optimisation of a bracket
  1. 4.1Mass and energy balance models
  2. 4.2Non-linear programming in chemical processes
  3. 4.3Process synthesis and superstructures
  4. 4.4Energy integration and pinch analysis
  5. 4.5Real-time optimisation
  6. 4.6Dynamic optimisation of process trajectories
  7. 4.7Model predictive control
  8. 4.8Parameter estimation
  9. 4.9IPOPT and similar solvers
  10. 4.10Lab: reactor optimisation with IPOPT
  1. 5.1Aggregate production planning
  2. 5.2MRP and finite capacity
  3. 5.3Parallel machine scheduling
  4. 5.4Flow shop and job shop
  5. 5.5Batch scheduling in process industries
  6. 5.6Sequence-dependent setup times
  7. 5.7Constraint programming
  8. 5.8Rescheduling after disruptions
  9. 5.9Integration with MES
  10. 5.10Workshop: batch sequencing with CP-SAT
  1. 6.1Vehicle routing problems
  2. 6.2Routing with capacities and time windows
  3. 6.3Logistics network design
  4. 6.4Dock assignment problems
  5. 6.5Load and stowage optimisation
  6. 6.6Railway timetable and rolling stock planning
  7. 6.7Fleet management
  8. 6.8Reverse logistics and returns collection
  9. 6.9Heuristics for large problems
  10. 6.10Practical: field maintenance routing
  1. 7.1Economic dispatch of generation
  2. 7.2Optimal power flow
  3. 7.3Grid expansion planning
  4. 7.4Storage management
  5. 7.5Microgrids and self-consumption
  6. 7.6Demand response
  7. 7.7Building optimisation
  8. 7.8Electricity markets and bidding
  9. 7.9Decarbonisation and long-term models
  10. 7.10Lab: optimal power flow with pandapower
  1. 8.1Water distribution networks
  2. 8.2Sewerage networks
  3. 8.3Infrastructure maintenance planning
  4. 8.4Multi-year budget allocation
  5. 8.5Telecommunications network optimisation
  6. 8.6Public service location
  7. 8.7Construction and resource planning
  8. 8.8Linear asset management
  9. 8.9Service indicators
  10. 8.10Case study: prioritising pipe renewal
  1. 9.1Evolutionary algorithms for design
  2. 9.2NSGA-II multi-objective genetic algorithms
  3. 9.3Differential evolution for continuous variables
  4. 9.4Particle swarm optimisation
  5. 9.5CMA-ES evolution strategies
  6. 9.6Hybridisation with local search
  7. 9.7Parallelising metaheuristics
  8. 9.8Constraint handling
  9. 9.9Statistical comparison of algorithms
  10. 9.10Workshop: multi-objective heat exchanger design with NSGA-II
  1. 10.1Discrete-event simulation in engineering
  2. 10.2Simulation-based optimisation
  3. 10.3Simulation metamodels
  4. 10.4Ranking and selection
  5. 10.5Simulation of queueing systems
  6. 10.6Validating simulation models
  7. 10.7Facility capacity analysis
  8. 10.8Simulation of logistics processes
  9. 10.9Commercial industrial simulation tools
  10. 10.10Practical: buffer optimisation with simulation
  1. 11.1From the model to the tool
  2. 11.2Interfaces for planners
  3. 11.3Integration with ERP and MES
  4. 11.4Performance and response times
  5. 11.5Model maintenance
  6. 11.6Change management
  7. 11.7Measuring benefits
  8. 11.8Solver licences and costs
  9. 11.9Technical documentation of the mathematical model
  10. 11.10Case study: planning tool in production
  1. 12.1Selecting the engineering problem
  2. 12.2System and data analysis
  3. 12.3Mathematical formulation of the problem
  4. 12.4Implementation and solution
  5. 12.5Validation with real cases
  6. 12.6Sensitivity analysis
  7. 12.7Implementation proposal
  8. 12.8Conclusions and improvement proposals
  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, IPOPT and Siemens Plant Simulation, in free, academic or evaluation versions. Real procedures are studied, such as optimising the operation of energy systems, production sequencing on the plant floor and optimal component design with surrogate models. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 50001, ISO 55000, ISO 22400-2, IEC 62264-1, ISO 31000, ISO 9001.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 50001, ISO 55000 and ISO 22400-2 applied to real cases, not as a theoretical annex.
Industry software:Gurobi, Pyomo, Google OR-Tools, IPOPT and Siemens Plant Simulation, in free, academic or evaluation versions.
Master's Thesis with defence:an engineering optimisation project, from system analysis and mathematical formulation to solution, validation with real cases and an implementation proposal, 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.

Select the problem
Select the engineering problem and analyse the system and its data.
Formulate the model
Write the mathematical formulation with its physical and logical constraints.
Implement and solve
Implement the model in Pyomo or OR-Tools and solve it with Gurobi or IPOPT.
Validate and test
Validate the results on real cases and run a sensitivity analysis.
Master's Thesis
Present the implementation proposal and improvements, defended before a panel.
Career opportunities

Where this takes you

Optimisation engineerOperations research analystProcess engineerProduction plannerEnergy systems engineerOperations consultant
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.

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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, IPOPT and Siemens Plant Simulation, 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.

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