Postgraduate Diploma in Mission Optimisation and Load Balancing
Online 800 h Postgraduate Diploma in Mission Optimisation and Load Balancing. 8 modules with VDA 5050 v2.1, ISO 3691-4:2023 and Google OR-Tools. Dedicated tutor and open enrolment.
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What you will be able to do
Model logistics missions with real constraints.
Formulate assignment and routing problems.
Implement dynamic mission dispatchers.
Balance load across robots, zones and stations.
Optimise the energy management of the fleet.
Evaluate policies through discrete event simulation.
Implement and monitor the optimiser in production.
Who it's for
- Logistics optimisation engineer who wants to specialise in Mission Optimisation and Load Balancing.
- Operations analyst who wants to specialise in Mission Optimisation and Load Balancing.
- Logistics data scientist who wants to specialise in Mission Optimisation and Load Balancing.
- Fleet systems engineer who wants to specialise in Mission Optimisation and Load Balancing.
- Operational research consultant who wants to specialise in Mission Optimisation and Load Balancing.
- Intralogistics improvement manager who wants to specialise in Mission Optimisation and Load Balancing.
- Graduates in engineering or sciences seeking to enter logistics.
What you'll work with
- VDA 5050 v2.1
- ISO 3691-4:2023
- ISO 22400-2:2014
- IEC 62264-1:2013
- ISO 8373:2021
- IEC 62443-3-3:2013
8 modules · 800 h
1.1Missions, tasks and subtasks1.2Time and priority constraints1.3Robot capability modelling1.4Route graphs and travel times1.5Mission duration estimation1.6Demand and time patterns1.7Historical operational data1.8Service metrics and service levels1.9Optimisation objectives1.10Workshop: warehouse mission model
2.1Integer linear programming2.2Assignment problem and the Hungarian method2.3Vehicle routing problems (VRP)2.4VRP with time windows2.5Pickup and delivery2.6Constructive insertion heuristics2.7Metaheuristics: tabu search and simulated annealing2.8Genetic algorithms for assignment2.9OR-Tools and Gurobi solvers2.10Practical: VRP with time windows in OR-Tools
3.1Online versus offline assignment3.2Greedy and rule-based policies3.3Auctions and market mechanisms3.4Reassignment on events3.5Rolling-horizon planning3.6Reinforcement learning for dispatching3.7Handling uncertainty3.8Fairness among robots3.9Computation time and scalability3.10Laboratory: dynamic dispatcher in Python
4.1Utilisation of robots and stations4.2Balancing across warehouse zones4.3Relocation of idle robots4.4Station queues and queuing theory4.5Bottlenecks and their detection4.6Balancing under demand peaks4.7Interaction with human staff4.8Network flow models4.9Priority and urgency rules4.10Case study: balancing picking stations
5.1AMR consumption models5.2Opportunity charging strategies5.3Charge scheduling5.4Number of chargers required5.5Battery degradation5.6Battery swapping versus charging5.7Electricity tariffs and timing5.8Integration with planning5.9Energy availability indicators5.10Practical: optimal charging policy
6.1Discrete event simulation6.2Fleet modelling in AnyLogic and FlexSim6.3Validation of the simulation model6.4Design of dispatch experiments6.5Sensitivity to fleet size6.6Comparison of dispatch policies6.7Statistical analysis of results6.8Digital twin of the operation6.9Presenting results to management6.10Laboratory: comparative policy simulation
7.1Integrating the optimiser with the FMS7.2Microservices architecture7.3Optimiser latency and robustness7.4KPI monitoring7.5A/B testing of policies7.6Handling operational exceptions7.7Continuous tuning with real data7.8Explainability for operators7.9Optimiser maintenance7.10Case study: deploying a new dispatcher
8.1Analysis of the current operation8.2Mission and fleet modelling8.3Problem formulation8.4Development of the assignment algorithm8.5Load balancing strategy8.6Simulation and comparison8.7Dispatcher implementation plan8.8Results and KPI report8.9Dissertation and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Google OR-Tools, Gurobi Optimizer, AnyLogic, FlexSim, Python with SimPy, in free, academic or evaluation versions. Tests are studied using real procedures for simulating dispatch policies, route optimisation with time windows, and fleet utilisation analysis with real data. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Model logistics missions with real constraints.
Formulate assignment and routing problems.
Implement dynamic mission dispatchers.
Balance load across robots, zones and stations.
Optimise the energy management of the fleet.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical standards documentation.
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 comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with full-time work.
You will work with Google OR-Tools, Gurobi Optimizer, AnyLogic, FlexSim, Python with SimPy. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM upon passing continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.
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