Integer and combinatorial optimization: basic concepts, cutting plane methods, branch-and-bound methods, branch-and-cut methods, heuristic methods.

Nonlinear optimization: basic concepts, optimality conditions, solution methods for convex and nonconvex optimization problems.

Dynamic and stochastic models and methods: Dynamic optimization, Bellman methods, lot-sizing models and dynamic and stochastic models of inventory, queues.

Learning Objectives:

The student 

• knows and describes the basic concepts of integer and combinatorial optimization, nonlinear optimization and dynamic optimization,
• knows the methods and models indispensable for a quantitative analysis,
• models and classifies optimization problems and selects appropriate solution procedures to solve simple optimization problems independently,
• validates, illustrates and interprets obtained solutions.

Workload:

The total workload for this course is structured as follows:

• Attendance at lectures/exercises (presence time) (5 SWS × 15 weeks): 75 h

• Preparation and follow-up of course content: 110 h

• Exam preparation and participation in the assessment: 85 h

Total workload: 270 h (corresponds to 9 ECTS credits)

• Nickel, Rebennack, Stein, Waldmann: Operations Research, 3. Auflage, Springer, 2022
• 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