Robust and Stochastic Optimization
Certificate Course - Management & Leadership - MM_MSc.5.4_MSc
| Date | Dec 17-18, 2026 |
| Duration | 2 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | upon request |
| Cost | 1,550 € |
Fundamentals
Understand principles of optimization under uncertainty, including robust and stochastic optimization concepts.
Technology
Apply quantitative optimization techniques and models to analyze uncertain decision-making environments.
Applications
Use optimization methods to solve complex real-world problems across interconnected business and engineering domains.
What you'll explore
- Understand uncertain decision problems and explore robust optimization approaches for handling different forms of uncertainty.
- Analyze interval and polyhedral uncertainty models to support reliable decision-making under incomplete information.
- Apply stochastic optimization methods, including deterministic equivalents, extensive forms, and scenario-tree approaches.
- Generate and evaluate scenarios to assess uncertainty and measure the value of stochastic solutions.
- Formulate multi-stage optimization problems and examine techniques such as chance constraints and stochastic dynamic programming.
Your key takeaways
- Understand how uncertainty influences decisions in optimization problems.
- Determine when uncertainty should be incorporated into optimization models.
- Apply robust and stochastic optimization techniques to model uncertainty effectively.
- Evaluate the advantages and limitations of robust and stochastic optimization approaches.
- Interpret solutions generated through robust and stochastic optimization methods.
Taught by recognized experts in Robust and Stochastic Optimization
Benefit from the knowledge of leading specialists with extensive experience in research and industry. Their deep expertise guarantees a course of outstanding academic and practical quality.
Prof. Dr. Steffen Rebennack
Prof. Dr. Steffen Rebennack is Professor of Stochastic Optimization at Karlsruhe Institute of Technology (KIT). His research focuses on advanced methods for decision-making under uncertainty, with applications in energy systems, operations research, and industrial optimization. He develops mathematical models and algorithms to improve complex planning and resource allocation problems. Before joining KIT, he held tenured and tenure-track positions at the Colorado School of Mines in the USA. With a strong international academic background in mathematics, industrial engineering, and management, he combines theoretical depth with applied problem-solving for real-world systems.
Anil Kaya
Anil Kaya, M. Sc is a Research Associate at Karlsruhe Institute of Technology (KIT), specializing in stochastic optimization and power systems modeling. His research focuses on developing advanced methods for energy system planning under uncertainty, including multi-objective optimization, agent-based simulation, and electricity market modeling. He has published in leading international journals such as IISE Transactions and the European Journal of Operational Research. With a strong background in engineering, logistics, and operations research, he bridges academic theory and industrial practice to support reliable, efficient, and sustainable energy systems.
Who should attend
This course is particularly beneficial for professionals in the following fields
- Optimization and Operations Professionals
Engineers, analysts, and specialists involved in quantitative modeling, optimization, and decision-making under uncertainty in complex systems. - Supply Chain and Risk Management Professionals
Managers, consultants, and operations professionals responsible for planning, resource allocation, and managing uncertainty in supply chains and business processes. - Data Analytics and Decision Support Professionals
Professionals working with stochastic models, simulation, and analytical tools to support strategic and operational decision-making. - Strategy and Planning Roles
Professionals at the interface of operations, engineering, and business strategy, engaged in long-term planning and optimization in uncertain environments. - Early-Career Professionals and Researchers
Graduates, PhD candidates, and academic staff in engineering, mathematics, operations research, or business seeking to strengthen competencies in robust and stochastic optimization.
Advance your career with KIT-level expertise
Benefit from the reputation of the Karlsruhe Institute of Technology (KIT) while gaining practical skills, flexible learning opportunities, and a recognized certificate to support your long-term professional growth.
Flexibility
Gain focused expertise in a specific field without committing to a full degree program, allowing you to build relevant knowledge efficiently and integrate learning seamlessly into your professional routine.
Relevance
Benefit from high-quality academic content combined with practical insights, delivered by experienced experts, supporting continuous, lifelong learning while ensuring direct applicability in real-world scenarios.
Advancement
Enhance your professional profile with a recognized certificate, demonstrating your commitment to ongoing development and supporting your career with tangible, verifiable credentials.