Recent Advances in Decision Support Systems (R-Module)
Exam number: 6722
Semester: from 2nd semester
Duration of the module: One semester
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Each summer 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. Maximum 15 participants.
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.): 15 h; self-study: 165 h
Contact hours (per week in semester): 1
Methods and duration of examination:
Successful preparation of a seminar paper of 10-15 pages including a small implementation project, an intermediate presentation at the end of the first and a final presentation at the end of the 2nd block (20-30 min. each)
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 seminar we want to investigate the state-of-the-art of 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.
Possible topics for the seminar paper are:
- Two-stage stochastic programming: modeling issues and an illustrative example
- Multi-stage stochastic programming: modeling issues and an illustrative example
- Stochastic dynamic programming: modeling issues and an illustrative example
- Robust optimization: modeling issues and an illustrative example
- Risk optimization: model formulations and an illustrative example
- Scenario (tree) generation and stability testing
- Implementing stochastic programming models with MOSEL
- Implementing stochastic programming models with Python
- Implementing stochastic programming models with COIN SFlopC++
- Stochastic programming approaches to the newsvendor problem
- Stochastic programming approaches to multiproduct assembly
- SP approaches to portfolio selection
- SP approaches to supply network design
Teaching and learning methods:
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 material.
Registration in Moodle Viadrina required.