Algorithms and OR


Solving large computational problems from various application areas such as production, logistics, traffic, and science requires the application of advanced analytical and algorithmic methods. With the rapidly increasing amount of a data, the problem instances that need to be solved become larger and larger. This so-called big data challenge makes even more important to invest into foundational and applied algorithmic research. Before one can choose the right algorithmic framework or design a specific algorithmic solution that is tailored to the problem at hand, a significant amount of effort must be spent into modeling and understanding a problem. All these steps require the rigorous application of mathematical methods.

The design and analysis of algorithms is a central topic in various research disciplines. Within computer science a focus lies on the design and analysis of algorithms with provable performance guarantees and in investigating the computational complexity of algorithmic problems. Operations Research (OR) deals with the application of advanced analytical and algorithmic methods to help make better decisions in economic contexts covering areas such as management science and business analytics.

Employing techniques from mathematical sciences, such as formal modeling, statistical analysis, and mathematical optimization, algorithm design and operations research arrives at optimal or near-optimal solutions to complex decision-making problems. It encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory, Markov decision processes, economic methods, data analysis, statistics, neural networks, expert systems, and decision analysis. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system. (With changes quoted from:

Main applications areas (adopted from the CFP of EURO 2009):

  1. Continuous optimization and control
  2. Data mining; knowledge discovery; artificial intelligence
  3. DEA and performance management
  4. Decision analysis; decision support systems; modelling languages
  5. Discrete optimization; graphs & networks
  6. Energy, environment & climate
  7. Financial modelling; risk management; banking
  8. Fuzzy sets; softcomputing
  9. Game theory; mathematical & experimental economics
  10. Health, life sciences & bioinformatics
  11. Location; logistics; transportation; traffic
  12. Metaheuristics & biologically inspired approaches
  13. Multiple criteria decision making, optimization & group decision
  14. OR education, history & ethics
  15. OR for developing countries
  16. OR in agriculture & natural resources
  17. OR in industries & software applications
  18. Production management; supply chain management
  19. Revenue management & managerial accounting
  20. Scheduling, time tabling & project management
  21. Stochastic programming; stochastic modelling; simulation
  22. System dynamics; dynamic modelling
  23. Telecommunication & network analysis



Peter Letmathe



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