Nowadays, everyone is talking about the tremendous opportunities offered by “big data” and all kinds of analytics. To really make the difference, though, you need to be able to turn data and analytical insights into better managerial decisions – and that requires rigorous quantitative tools.
WHO SHOULD JOIN?
Students and professionals in the field of Engineering, Computer Science, Physics, Mathematics or Quantitative Business Studies. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to students and professionals with a wide variety of backgrounds.
ADDITIONAL ENTRY REQUIREMENTS
Students are assumed to have completed undergraduate courses in linear algebra and analysis. Basic knowledge of probability theory is also required.
This course delivers those tools, introducing you to the most successful models and algorithms from operations research (OR), including (integer) linear optimization, network optimization, stochastic optimization and heuristics. Not only do you learn some of the beautiful but basic mathematics behind them, but during computer practicals you gain hands-on experience with up-to-date software applied to practical cases in such domains as logistics and revenue management. The course will enable you to recognize and exploit opportunities for mathematically supported decision making and can help prepare you for an MSc in Operations Research.
Specific topics include:
• The world of optimization, considering both deterministic and stochastic problems (that is, with and without data uncertainty).
• Modelling optimization problems using powerful tools such as integer programming.
• Some insights into the theory that drives the effectiveness of these tools.
• The use of optimization software, such as Matlab, Python, and Gurobi.
• Algorithms for key problems in network optimization, such as finding the cheapest tour through a network.
• Understanding stochastic processes like Markov chains to model uncertainty in operational systems.
• Queueing models and queueing networks.
• Stochastic dynamic programming techniques to determine optimal decisions in operational problems.
• Stochastic computer simulation techniques, enabling you to model and analyse realistic problems in operational systems.
At the end of this course, you:
- Can model a practical optimization problem into an appropriate mathematical formulation.
- Can solve the mathematical model using advanced optimization software.
- Have a knowledge of network optimization problems and the algorithms to solve them.
- Have a knowledge of optimization theory and integer linear programming techniques
- Can model uncertainty in operational systems as a stochastic process.
- Can simulate a stochastic process using simulation software.
Possible visit to a leading consultancy firm in operations research (to be confirmed).