Stochastic Modeling and Optimization (Master and PhD Program)

(Prof. Dr. Stefan Minner, Larkin Liu)

Monday 9:45-11:15 in 0534 & Wednesday 9:45-11.15 in N1090



Please note that we currently plan to give the lecture on-site / in person, given the development of the pandemic situation. Enrolled students will be timely notified of any changes.


Exam re-take scheduled for 26.07.2022 - 12:30 to 14:00 - location TBD.


  • Uncertainty Modeling: Probability Theory, Stochastic Processes, Fuzzy Set Theory, Newsvendor Problems, Bayes Updating, Forecast Evolution
  • Stochastic Dynamic Programming and Approximate Dynamic Programming
  • Markov Chains and Markov Decision Processes: LP, Value Iteration, Policy Iteration
  • Stochastic Programming: Chance Constrained Programming, Two-Stage Models with Recourse, Sample Average Approximation, Sampling Strategies, Data-Driven Optimization-Machine Learning Interface
  • Simulation Optimization
  • Applications: Queuing Theory, Queuing Networks, Factory Physics, Inventory Theory (Single-Echelon, Multi-Echelon, Stochastic Demand, Stochastic Price)


The course focuses on stochastic modeling and optimization methods for decision support and covers recent research contributions in several fields of logistics and operations.


The topics of the course will be introduced using state-of-the-art overview articles and then be highlighted by the study of recent research papers in the respective field. The objective is to give both, an overview of research fields, typical research methodology, and to inspire own work in the field.


  • The recommended programming language for this course is Python, and Python open-source libraries such as numpy and scipy, which are fully sufficient for this course.
  • As an alternative, for mathematical programming computations, Gurobi is also recommended for MIP and LP solvers.


  • Lectures start on: tba
  • Final exam: tba


  • Tijms, H.C. (2003). A First Course in Stochastic Models. Wiley.
  • King, A.J., Wallace, S.W. (2012). Modeling with Stochastic Programming. Springer.
  • Powell, W. (2011). Approximate Dynamic Programming. Wiley.
  • Kleijnen, J.P.C. (2008). Design and Analysis of Simulation Experiments. Springer.
  • For additional readings please see lecture slides.