Diploma in MILP/CP Modeling and Heuristics for Production
Online postgraduate diploma of 800 hours in MILP/CP modelling and heuristics for production. 8 modules covering production scheduling problems, mixed-integer linear programming, formulation improvement, constraint programming, constructive heuristics, metaheuristics and shop-floor deployment in line with IEC 62264 and ISA-88, 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
Formalise production planning and sequencing problems.
Model production problems with mixed-integer linear programming.
Improve formulations and apply decomposition techniques.
Solve scheduling problems with constraint programming.
Design effective constructive heuristics.
Apply metaheuristics to large-scale problems.
Integrate optimisation models into real-world planning.
Who it's for
- Operations research engineers.
- Advanced production planners who want to move beyond spreadsheets and rules of thumb.
- Decision scientists.
- APS (advanced planning and scheduling) system developers.
- Industrial optimisation consultants.
- Industrial organisation engineers.
- Engineering or science graduates seeking to enter the chemical industry.
What you'll work with
- IEC 62264 (ISA-95: enterprise-control integration)
- ISA-88 (Batch control)
- MPS format (Mathematical Programming System)
- MiniZinc 2.x (Constraint modelling language)
- ISO 22400-2 (KPIs for manufacturing operations management)
- Gurobi
- Google OR-Tools CP-SAT
- IBM ILOG CPLEX and CP Optimizer
- Pyomo
- HiGHS
8 modules
1.1Planning, scheduling and sequencing1.2Classification of shop problems: flow shop and job shop1.3Graham notation for scheduling1.4Objective functions: makespan, tardiness and costs1.5Computational complexity and NP-hardness1.6Common plant constraints1.7Horizon, granularity and rescheduling1.8Required data and its quality1.9Choosing between exact and approximate methods1.10Workshop: formalising a sequencing problem
2.1Variables, constraints and objective2.2Formulations with binary variables2.3Big-M constraints and their risks2.4Discrete-time and continuous-time formulations2.5Lot sizing: CLSP2.6Branch and bound2.7Cutting planes and relaxations2.8Reading the gap and the solver log2.9Modelling with Pyomo and PuLP2.10Practical exercise: lot-sizing model with Pyomo
3.1Strong and weak formulations3.2Symmetry breaking3.3Valid inequalities and cuts3.4Benders decomposition3.5Column generation3.6Lagrangian relaxation of coupling constraints3.7Solver parameters and tuning3.8Warm starts3.9Comparison of commercial and open-source solvers3.10Lab: tuning a MILP with Gurobi
4.1Finite-domain variables and propagation4.2Global constraints: alldifferent and cumulative4.3Interval variables4.4No-overlap constraints4.5Sequence-dependent setup times4.6Search strategies4.7OR-Tools CP-SAT4.8IBM CP Optimizer4.9CP versus MILP in scheduling4.10Practical exercise: job shop with CP-SAT
5.1Dispatching rules: SPT, EDD and ATC5.2Johnson's algorithm5.3NEH heuristic for flow shop5.4Greedy heuristics with priorities5.5Insertion heuristics5.6Shifting bottleneck heuristics5.7Quality assessment against bounds5.8Speed versus quality5.9Integration with business rules5.10Lab: comparison of dispatching rules
6.1Local search and neighbourhoods6.2Simulated annealing and cooling schedules6.3Tabu search and memory structures6.4Genetic algorithms with permutation encoding6.5Iterated local search6.6Large Neighbourhood Search6.7Matheuristics: MILP inside the heuristic6.8Parameter tuning with irace6.9Taillard benchmarks6.10Practical exercise: tabu search for flow shop
7.1Integration with ERP and MES7.2Calendars, shifts and maintenance7.3Rescheduling in response to events7.4Plan stability7.5User interface for planners7.6Explaining the model's decisions7.7Validation with expert planners7.8Measuring the benefit7.9Industrial scheduling cases7.10Case study: scheduler for a packaging line
8.1Description of the production system8.2Problem formulation8.3MILP model of production8.4CP model of production8.5Heuristic or metaheuristic8.6Experimental comparison of the methods8.7Integration proposal8.8Conclusions and future work8.9Report 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 Gurobi, Google OR-Tools CP-SAT, IBM ILOG CPLEX and CP Optimizer, Pyomo and HiGHS, in free, academic or evaluation versions. Practical work is based on real procedures for experimental comparison of MILP, CP and heuristics, solver parameter tuning on real instances and validation of production schedules with planners. Assessment is continuous by module plus the final project.
Standards covered: IEC 62264, ISA-88, MPS format, MiniZinc 2.x, ISO 22400-2.
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 Gurobi, Google OR-Tools CP-SAT, IBM ILOG CPLEX and CP Optimizer, Pyomo and HiGHS, 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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