Decision Support under Uncertainty
Exam number: 6732
Semester: from 1st semester
Duration of the module: One semester
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Each winter semester
Prerequisites: Simultaneous or previous participation in the track modules "Operations Research" or "Management Science", good knowledge and deep interest in mathematical modeling and quantitative methods.
Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.
Person responsible for module: Prof. Dr. Achim Koberstein
Name of the professor: Prof. Dr. Achim Koberstein
Language of teaching: English
ECTS-Credits (based on the workload): 6
Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 45 h; self-study: 135 h
Contact hours (per week in semester): 4
Methods and duration of examination:
Successful written exam (120 min), bonus point from exercise sheets
Emphasis of the grade for the final grade: Please check regulations of the study programme
Aim of the module (expected learning outcomes and competencies to be acquired):
The participants will learn how to build model-based decision support systems for making and planning business decisions under uncertainty and risk.
Contents of the module:
Many managerial planning and decision problems are characterized by uncertainty and risk. Recently, great progress has been made in taking into account uncertain data explicitly in model-based decision support systems to produce more flexible and robust planning solutions. In this lecture, we will introduce the two most important approaches in this respect, namely stochastic programming and robust optimization, from a modeling and systems point of view. Also, we want to get acquainted with modelling languages for stochastic optimization and learn how to implement small illustrative applications ourselves.
Planned contents are:
- Recapitulation of deterministic linear and mixed integer modelling
- Two-stage stochastic programming: modeling issues and illustrative examples
- Multi-stage stochastic programming: modeling issues and illustrative examples
- Stochastic dynamic programming: modeling issues and illustrative examples
- Robust optimization: modeling issues and illustrative examples
- Risk optimization: model formulations and illustrative examples
- Scenario (tree) generation and stability testing
- Modelling languages for implementing stochastic programming models
- Applications in operations management and finance
Teaching and learning methods:
Lectures, exercises, hands-on work, self-studies
Literature (compulsory reading, recommended literature):
John R. Birge, Francois Louveaux. Introduction to Stochastic Programming. Springer, 1997.
Alan J. King, Stein W. Wallace. Modeling with Stochastic Programming. Springer, 2012.
Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczynski. Lectures on Stochastic Programming. SIAM, 2009.
Laurent El Ghaoui, Aharon Ben-Tal, Arkadi Nemirovski. Robust optimization. Princeton Series in Applied Mathematics. Princeton University Press, 2009.
Additional topic specific and proprietary material.
Registration in Moodle Viadrina required.