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.
Master’s in Optimisation and OR for Engineering — studying from New Zealand
New Zealand's data and artificial intelligence sector is growing, with strong activity in Auckland, Wellington and Christchurch across fintech, agritech, health analytics and government data services. Universities including the University of Auckland, the University of Otago and Victoria University of Wellington run leading research in machine learning and computational modelling. Simulation and analytics skills are widely applied in agriculture, energy and environmental modelling, and employers include government agencies such as Stats NZ as well as technology firms and consultancies.
Fees for New Zealand: NZ$15,700 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
Are data and AI engineering roles regulated in New Zealand, and how does that affect a private qualification?
Data and AI are not regulated professional titles in New Zealand, though privacy obligations fall under the Office of the Privacy Commissioner. Engineers who want chartered status can pursue registration through Engineering New Zealand. SEIUM's qualifications are private, not state degrees, and not accredited by a national body, so employers decide how much they value them.
Which New Zealand employers hire data, AI and simulation engineers?
Demand is found in banks and insurers, government agencies, healthcare, energy and utilities, technology firms, and consultancies. Employers usually assess portfolios, practical projects and technical interviews more than credentials. A private online programme can help build relevant skills, but it does not guarantee a position or a specific salary.
Can I complete this online from New Zealand in English while working?
Yes. The programmes are fully online and taught in English, so you can study from New Zealand without a student visa. You will have an assigned technical tutor and complete a final project. Specialist courses are 300 hours, diplomas 800 and master's 1,900. Fees can be paid in instalments, and some employers sponsor study, so ask yours early.
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.
What you will be able to do
Formulate engineering problems as mathematical optimisation models.
Solve linear, integer and non-linear programming models.
Optimise the design of components and processes.
Plan and sequence industrial operations.
Apply optimisation to energy systems and infrastructure.
Combine simulation and optimisation in complex systems.
Implement operations research tools in organisations.
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.
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
12 modules · 1900 h
1.1Engineering problems as optimisation problems1.2Design, operation and planning1.3Formulating models with physical constraints1.4Time scales and decision levels1.5Modelling languages: Pyomo, JuMP and AMPL1.6The solver ecosystem1.7Model reproducibility1.8Reference cases in engineering1.9Input data quality1.10Case study: formulating the design of a pipe network
2.1Flow models in physical networks2.2Formulating logical constraints with big-M2.3Linearising non-linearities2.4Unit commitment models2.5Equipment sizing with integer variables2.6Location and capacity models2.7Solver tuning and tolerances2.8Symmetry and alternative formulations2.9Decomposition for large models2.10Lab: thermal unit commitment with MILP
3.1Parametric and shape optimisation3.2Density-based SIMP topology optimisation3.3Gradient methods with sensitivity analysis3.4The adjoint method for design gradients3.5Multidisciplinary optimisation of coupled systems3.6Surrogate models and kriging3.7Bayesian optimisation with acquisition functions3.8Manufacturability constraints3.9Coupling with finite elements3.10Practical: topology optimisation of a bracket
4.1Mass and energy balance models4.2Non-linear programming in chemical processes4.3Process synthesis and superstructures4.4Energy integration and pinch analysis4.5Real-time optimisation4.6Dynamic optimisation of process trajectories4.7Model predictive control4.8Parameter estimation4.9IPOPT and similar solvers4.10Lab: reactor optimisation with IPOPT
5.1Aggregate production planning5.2MRP and finite capacity5.3Parallel machine scheduling5.4Flow shop and job shop5.5Batch scheduling in process industries5.6Sequence-dependent setup times5.7Constraint programming5.8Rescheduling after disruptions5.9Integration with MES5.10Workshop: batch sequencing with CP-SAT
6.1Vehicle routing problems6.2Routing with capacities and time windows6.3Logistics network design6.4Dock assignment problems6.5Load and stowage optimisation6.6Railway timetable and rolling stock planning6.7Fleet management6.8Reverse logistics and returns collection6.9Heuristics for large problems6.10Practical: field maintenance routing
7.1Economic dispatch of generation7.2Optimal power flow7.3Grid expansion planning7.4Storage management7.5Microgrids and self-consumption7.6Demand response7.7Building optimisation7.8Electricity markets and bidding7.9Decarbonisation and long-term models7.10Lab: optimal power flow with pandapower
8.1Water distribution networks8.2Sewerage networks8.3Infrastructure maintenance planning8.4Multi-year budget allocation8.5Telecommunications network optimisation8.6Public service location8.7Construction and resource planning8.8Linear asset management8.9Service indicators8.10Case study: prioritising pipe renewal
9.1Evolutionary algorithms for design9.2NSGA-II multi-objective genetic algorithms9.3Differential evolution for continuous variables9.4Particle swarm optimisation9.5CMA-ES evolution strategies9.6Hybridisation with local search9.7Parallelising metaheuristics9.8Constraint handling9.9Statistical comparison of algorithms9.10Workshop: multi-objective heat exchanger design with NSGA-II
10.1Discrete-event simulation in engineering10.2Simulation-based optimisation10.3Simulation metamodels10.4Ranking and selection10.5Simulation of queueing systems10.6Validating simulation models10.7Facility capacity analysis10.8Simulation of logistics processes10.9Commercial industrial simulation tools10.10Practical: buffer optimisation with simulation
11.1From the model to the tool11.2Interfaces for planners11.3Integration with ERP and MES11.4Performance and response times11.5Model maintenance11.6Change management11.7Measuring benefits11.8Solver licences and costs11.9Technical documentation of the mathematical model11.10Case study: planning tool in production
12.1Selecting the engineering problem12.2System and data analysis12.3Mathematical formulation of the problem12.4Implementation and solution12.5Validation with real cases12.6Sensitivity analysis12.7Implementation proposal12.8Conclusions and improvement proposals12.9Thesis report12.10Master's Thesis: defence before a panel
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.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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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