Optimization Under Uncertainty - 096335
Will be learned this year
Determination of the grade according to progress during the semester and the submission of the final thesis
||Nonlinear Models in Operations Research||
The Course Will Review Modeling and Solution Methods for Optimization Under Uncertainty. the Course Includes the Following Topics: Robust and Distributionally Robust Optimization, Stochastic Optimization, Chance Constraints, Data-Driven Optimization, Solution Methods Including Robust-Counterparts and Iterative Methods. at the End of the Course the Student Will Be Able to:
1. Understand the Challenges of Modeling and Solving Optimization Problems with Uncertain Parameters, Including the Limitations of the Various Modeling Techniques and the Computational Challenges Associated with the Various Solution Methods.
2. Formulate Robust Optimization Models for Single- and Multi-Stage Optimization Problems with Uncertainty.
3. Solve Different Robust Optimization Models by Using Robust Counterparts and Iterative Methods.
4. Understand How to Incorporate Data in Modeling Uncertainty and the Statistical Meaning of Such Models.
5. Implement the Methods Learned on Real-World Optimization Problems with Uncertainty, through the Characterization of the Uncertainty and Identification of the Appropriate Modeling and Solution Techniques.
Timetable to semester 02/2020
2020/2021 Spring Semester
| || ||15:30-18:30||Monday||Doctor Shtern Shimrit||Lecture||10||11|
| || ||18:30-19:30||Monday||Mr. Gur Eyal||Exercise||11|
|2009||princeton university press||ben-tal, aharon, laurent el ghaoui, and||robust optimization|
Created in 06/03/2021 Time 01:42:45