Drive smarter decisions and improve business outcomes by mastering optimisation techniques with Python. This practical course teaches you how to model and solve real-world decision-making problems, ranging from resource allocation to production planning and logistics.
You will learn to formulate and solve optimization problems using proven methods, such as linear programming, integer programming, and nonlinear optimization. With easy-to-use Python libraries like PuLP and Pyomo, you will gain hands-on experience building models that deliver measurable value.
Designed for professionals with some background in programming and mathematics, this course will help you apply optimisation to make better, data-driven decisions in finance, supply chain, operations, and strategy. By the end, you will confidently develop and communicate optimisation solutions to support your organisation’s goals.
Key Topics:
- Day 1: Review of linear and mixed-integer linear optimisation (case study: Recharging strategy for electric vehicles)
Day 2: Network optimisation: models and heuristics (case study: Arbitrage search in cryptocurrency markets)
Day 3: Accounting for uncertainty: “Optimisation meets reality” (case study: Fleet assignment and delays)
Day 4: Robust optimisation 1 (case study: Production plan accounting for uncertainty)
Day 5: Robust optimisation 2 (case study: City routing accounting for traffic disruptions)
Day 6: Stochastic optimisation 1 (case study: distribution centre stock optimisation)
Day 7: Stochastic optimisation 2 (case study: Investment portfolio optimisation)
Day 8: Stochastic optimisation 3 (case study: Two-stage land allocation problem with uncertain yield)
Participants will leave with the skills to model, solve, and interpret complex optimisation problems in uncertain environments, gaining practical experience with cutting-edge techniques and tools.
Key Deliverables:
- Case studies to connect course concepts to real-world problems
- Daily assignments to reinforce theoretical and practical understanding
This advanced course provides participants with a comprehensive understanding of robust and stochastic optimisation, equipping them with the skills to tackle uncertainty in optimisation problems across diverse industries.
Practical programme information:
Starting date: To be determined
Tuition fee: € 4,995
Format: This course consists of 8 full days of study.
Participants are expected to work at home for 4 to 8 hours per week
Group size: 8-18
Location: This programme is exclusively offered in person at VU Amsterdam.
(In case of insufficient admissions, the organisation reserves the right to postpone the course)
For more information about this course, please contact: BAforIndustry@vu.nl
This course is part of the Business Analytics for Industry programme.
It includes two other courses:
