Integer and Combinatorial Programming: Basic notions, cutting plane metehods, branch and bound methods, branch and cut methods, heuristics.

Nonlinear Programming: Basic notions, optimality conditions, solution methods for convex and nonconvex optimization problems.

Dynamic and stochastic models and methods: dynamical programming, Bellman method, lot sizing models, dyanical and stochastic inventory models, queuing theory.

Learning objectives:

The student

• names and describes basic notions of integer and combinatorial optimization, nonlinear programming, and dynamic programming,
• knows the indispensable methods and models for quantitative analysis,
• models and classifies optimization problems and chooses the appropriate solution methods to solve optimization problems independently,
• validates, illustrates and interprets the obtained solutions.

• Nickel, Stein, Waldmann: Operations Research, 2. Auflage, Springer, 2014
• Hillier, Lieberman: Introduction to Operations Research, 8th edition. McGraw-Hill, 2005
• Murty: Operations Research. Prentice-Hall, 1995
• Neumann, Morlock: Operations Research, 2. Auflage. Hanser, 2006
• Winston: Operations Research - Applications and Algorithms, 4th edition. PWS-Kent, 2004