Diploma in Policy Design with Monte Carlo Simulation
Online postgraduate diploma of 800 hours in policy design with Monte Carlo simulation. 8 modules covering Monte Carlo fundamentals, uncertainty modelling, variance reduction, policy evaluation, simulation-based optimisation, dynamic system simulation and risk communication in line with JCGM 101:2008, ISO 31000:2018 and IEC 31010:2019, with an assigned technical tutor.
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
Build correct, reproducible Monte Carlo simulations.
Model input uncertainty with distributions and dependencies.
Apply variance reduction techniques.
Define and compare decision policies through simulation.
Optimise policy parameters with simulation-based methods.
Combine Monte Carlo with simulation of dynamic systems.
Communicate risk results for decision-making.
Who it's for
- Simulation analysts.
- Operations research engineers.
- Risk analysts.
- Supply chain planners setting inventory and replenishment policies.
- Asset management engineers.
- Quantitative decision consultants.
- Engineering or science graduates seeking to enter logistics.
What you'll work with
- JCGM 101:2008 (Propagation of distributions using a Monte Carlo method)
- ISO 31000:2018 (Risk management)
- IEC 31010:2019 (Risk assessment techniques)
- ISO 28640:2010 (Random variate generation methods for Monte Carlo)
- ISO 55001:2014 (Asset management)
- Python with NumPy and SimPy
- Oracle Crystal Ball
- Palisade @RISK
- AnyLogic
- Optuna
8 modules
1.1Origin and principle of the method1.2Pseudorandom number generators1.3Sampling distributions: inversion and acceptance-rejection1.4Law of large numbers and standard error1.5Number of replications required1.6Confidence intervals for results1.7Seeds and reproducibility1.8Monte Carlo in a spreadsheet and in Python1.9Common errors in Monte Carlo simulations1.10Workshop: first simulation of a process under uncertainty
2.1Choosing input distributions2.2Fitting distributions to data2.3Goodness-of-fit tests2.4Subjective distributions and expert elicitation2.5PERT and triangular distributions2.6Dependencies and correlations2.7Gaussian and t copulas2.8Epistemic and aleatory uncertainty2.9Input validation2.10Practical exercise: elicitation and fitting of distributions
3.1Latin hypercube sampling3.2Correlated antithetic variates3.3Control variates3.4Importance sampling3.5Rank-stratified sampling3.6Common random numbers3.7Quasi-Monte Carlo with Sobol sequences3.8Rare-event simulation3.9Efficiency comparison3.10Lab: Latin hypercube versus simple random sampling
4.1Policy as a decision rule4.2Inventory policies (s, S) and (R, Q)4.3Age-based and block maintenance policies4.4Capacity and staffing policies4.5Metrics: expected cost, service level and risk4.6Statistical comparison of policies4.7Selecting the best system (ranking and selection)4.8Risk constraints in evaluation4.9Finite- and infinite-horizon simulation4.10Practical exercise: comparison of inventory policies
5.1Optimisation via simulation5.2Grid and random search5.3Response surface methods5.4Bayesian optimisation with Gaussian processes5.5Evolutionary algorithms with simulation5.6Stochastic approximation (SPSA)5.7Noise in the objective function5.8Allocating the computational budget5.9OptQuest and Optuna tools5.10Lab: Bayesian optimisation of a maintenance policy
6.1Discrete-event simulation6.2Monte Carlo simulation over time6.3Markov processes and chains6.4System dynamics6.5Agent-based simulation6.6Combining paradigms6.7Model verification and validation6.8Warm-up and steady state6.9Parallelising replications6.10Case study: spare parts policy with discrete events
7.1Visualising output distributions7.2Exceedance curves and percentiles7.3Risk analysis for management7.4Documenting assumptions7.5Peer review of the model7.6Version control of simulation models7.7Responsible use of results7.8Simulation in recurring decision processes7.9Industrial policy cases7.10Workshop: risk report for a management committee
8.1Problem and current policy8.2Simulation model8.3Characterising uncertainty8.4Designing alternative policies8.5Evaluation and comparison8.6Parameter optimisation8.7Risk analysis of the policy8.8Policy recommendation8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with NumPy and SimPy, Oracle Crystal Ball, Palisade @RISK, AnyLogic and Optuna, in free, academic or evaluation versions. Practical work is based on real procedures for statistical comparison of alternative policies, optimisation of policy parameters through simulation and efficiency analysis of variance reduction techniques. Assessment is continuous by module plus the final project.
Standards covered: JCGM 101:2008, ISO 31000:2018, IEC 31010:2019, ISO 28640:2010, ISO 55001:2014.
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
- Prior technical training or professional experience in the sector. No university degree is required.
- 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python with NumPy and SimPy, Oracle Crystal Ball, Palisade @RISK, AnyLogic and Optuna, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme 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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