Operations Analytics & Simulation
Integrated course, 3.00 ECTS
• Illustration and application of real decision making problems or simulation model
• Development of an algorithm to solve the problem
• mathematical optimisation
• Optimisation of transport and loading
• Graph theory and critical path analysis (optimising routes, spanning trees, Simplex applied to graph theory, CPM, MPM, PERT)
• Queuing theory and simulation
• Qualitative and quantitative forecasting systems
• IT systems to solve complex optimisation issues
• Predictive Analytics
The students are able to
• apply key methods of operations research to solve industrial problems.
• derive small to moderate potential for optimisation, for companies without using individualised software.
• explain the context for optimisation
• complement analysis to support decision-making by means of decision trees, simulations and forecasts.
• adapt analytical methods for lean manufacturing.
Recommended or required reading and other learning resources / tools
•Bill Franks: The Analytics Revolution: How to Improve Your Business By Making Analytics Operational In The Big Data Era
•Eric Siegel: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
•Foster Provost/Tom Fawcett: Data Science for Business: What you need to know about data mining and data-analytic thinking
The lecturer agrees to communicate an updated list of recommended literature in accordance with the syllabus.
• European Journal on Operations Research
Mode of delivery
Prerequisites and co-requisites
Bachelor's degree in either Industrial Engineering or any other field of Engineering and Technology.
Assessment methods and criteria
Final exam & continuous assessment