Stochastic Optimization (also called Stochastic Programming) is concerned with optimization problems under uncertainty. In such optimization problems, some of the input data are not known with certainty at the time of decision making. In Stochastic Optimization it is assumed that we have information about the distribution of these uncertain input parameters. This information in then incorporated into the mathematical programming models to yield optimal decisions under uncertainty.

• Stochastic Optimization / Stochastic Programming
• Power Systems Modeling and Optimization
• Decomposition Methods
• Large-scale Optimization
• Global Optimization
• Mixed-Integer (Non-)Linear Optimization / Mixed-Integer (Non-)Linear Programming
• Calculation of Piecewise Linear Functions

• BMWK: "ARTESIS -Systematically assess the robustness and adaptivity of energy system transformation pathways by comprehensively considering long-term, systemics uncertainties"  (01/24 - 12/26)

• MathSEE: "Theoretical and Empirical Analysis of Matheuristics” (10/2022 – 09/2025)

• PGMO: "Decomposition Methods for the Non-Linear Optimal Power Flow Problem“ (9/21-8/24)

• BMWi: "En4U – Pathways of a decentralized energy system considering decisions of private and commercial actors under uncertainty" (4/21-7/24)
• DFG: "COmPwise: Computing Optimal Piecewise Linear Functions and Their Applications" (6/21-5/25)

• EOSC Secretariat "Optimal Spatiotemporal Antivial Release under Uncertainty" (10/20-4/21)
• German Scholars Organization (GSO) and Carl-Zeiss-Stiftung (CZS): Wissenschaftler-Rückkehrprogramm (04/18-03/20)

• PGMO: "Decomposition Methods for the Non-Linear Optimal Power Flow Problem“ (9/21-8/24)

• BMWi: "En4U – Pathways of a decentralized energy system considering decisions of private and commercial actors under uncertainty" (4/21-7/24)

• John Warwicker and Steffen Rebennack, "Efficient Continuous Piecewise Linear Regression for Linearising Univariate Non-Linear Functions", "IISE Transactions"
• Christian Füllner and Steffen Rebennack, "Non-convex Nested Benders Decomposition", Mathematical Programming, 196: 987-1024, 2022
• Steffen Rebennack and Vitaliy Krasko, "Piecewise Linear Function Fitting via Mixed-Integer Linear Programming", INFORMS Journal on Computing, 32(2):507-530, 2020
• Timo Lohmann and Steffen Rebennack, "Tailored Benders Decomposition for a Long-Term Power Expansion Model with Short-Term Demand Response", Management Science, 63(6):2027-2048, 2017
• Gregory Steeger and Steffen Rebennack, "Dynamic Convexification within Nested Benders Decomposition using Lagrangian Relaxation: An Application to the Strategic Bidding Problem", European Journal of Operational Research, 257(2):669-686, 2017
• Steffen Rebennack, "Combining Sampling-based and Scenario-based Nested Benders Decomposition Methods: Application to Stochastic Dual Dynamic Programming", Mathematical Programming, 156(1):343-389, 2016
• Steffen Rebennack, "Computing Tight Bounds via Piecewise Linear Functions through the Example of Circle Cutting Problems", Mathematical Methods of Operations Research, 84(1):3-57, 2016
• Timo Lohmann, Amanda S. Hering, and Steffen Rebennack, "Spatio-Temporal Hydro Forecasting of Multireservoir Inflows for Hydro-Thermal Scheduling", European Journal of Operational Research, 255(1):243-258, 2016
• Stephen M. Frank and Steffen Rebennack, "Optimal Design of Mixed AC-DC Distribution Systems for Commercial Buildings: A Nonconvex Generalized Benders Decomposition Approach", European Journal of Operational Research, 242(3):710-729, 2015
• Steffen Rebennack, "Generation Expansion Planning under Uncertainty with Emissions Quotas", Electric Power Systems Research, 114:78-85, 2014

A complete list of publications is available here.