Photo of Nils

About Me

Hi, I am Nils Löhndorf! I am an Associate Professor at the Luxembourg Centre for Logistics and Supply Chain Management within the University of Luxembourg and Chairholder in Digital Procurement. As researcher and entrepreneur, I help decision-makers to make better in decisions in the face of uncertainty.
My research interest is in developing approximate dynamic programming methods and models for decision-making under uncertainty. In recent years I have developed a range of models for operation, valuation, and trading of energy storage systems that are used by several energy companies in Europe.

QUASAR Optimizer

Based on my research on stochastic optimization, I have developed a general-purpose solver for stochastic-dynamic optimization called QUASAR. The solver is intended for analysts, decision-makers, and researchers who want to solve difficult sequential decision problems that involve uncertainty. You can use QUASAR to solve linear multistage stochastic programs, continuous Markov decision processes, or stochastic-dynamic programs. QUASAR features

  • an algebraic modeling language for expressing continuous-state, finite-horizon, stochastic-dynamic decision problems.
  • a solution engine that combines scenario tree generation, approximate dynamic programming, and risk measures.
  • various functions and data structures to store, analyze, and visualize the optimal stochastic solution.

QUASAR provides native interfaces for Python and Java. If you want to try QUASAR go to quantego.com and sign up for a free trial. QUASAR is entirely free for academic usage.

Publications

  • Dimoski J, Fleten SE, Löhndorf N, Nersten S. 2023. Dynamic hedging for the real option management of hydropower production with exchange rate risks. Forthcoming in OR Spectrum. Download
  • Avila D, Papavasiliou A, Löhndorf N. 2023. Batch learning SDDP for long-term hydrothermal planning. IEEE Transactions on Power Systems. DOI: 10.1109/TPWRS.2023.3246724. Download
  • Seranilla BK, Löhndorf N. 2022. Optimizing vaccine distribution in developing countries under natural disaster risk. Working paper. Download
  • Löhndorf N, Wozabal D. 2022. The value of coordination in multi-market bidding of grid energy storage. Operations Research 71(1), 1-22. Download
  • Avila D, Papavasiliou A, Löhndorf N. 2021. Batch learning in stochastic dual dynamic programming. Available on Optimization Online. Download
  • Avila D, Papavasiliou A, Löhndorf N. 2021. Parallel and distributed computing for stochastic dual dynamic programming. Computational Management Science. Open access.
  • Löhndorf N, Wozabal D. 2020. Gas storage valuation in incomplete markets. European Journal of Operational Research 288(1), 318-330. Preprint available on Optimization Online. Download
  • Löhndorf N, Shapiro A. 2019. Modeling time-dependent randomness in stochastic dual dynamic programming. European Journal of Operational Research 273(2), 650-661. Preprint available on Optimization Online. Download
  • Nersten S, Dimoski J, Fleten S-E, Löhndorf N. 2018. Hydropower reservoir management using a multi-factor price model with correlation between price and local inflow. In Proceedings of the 41st IAEE International Conference. International Association for Energy Economics.
  • Löhndorf N. 2016. An empirical analysis of scenario generation methods for stochastic optimization. European Journal of Operational Research 255(1), 121-132. Download
  • Löhndorf N, Riel M, Minner S. 2014. Simulation optimization for the stochastic economic lot scheduling problem with sequence-dependent setup times. International Journal of Production Economics 157, 170-176. Download
  • Löhndorf N, Wozabal D, Minner S. 2013. Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Operations Research 61, 810-823. Download
  • Löhndorf N, Minner S. 2013. Simulation optimization for the stochastic economic lot scheduling problem. IIE Transactions 45, 796-810. Download
  • Transchel S, Minner S, Kallrath J, Löhndorf N, Eberhard U. 2011. A hybrid general lot-sizing and scheduling formulation for a production process with a two-stage product structure. International Journal of Production Research 49(9), 2463-2480. Download
  • Francas D, Löhndorf N, Minner S. 2011. Machine and labor flexibility in manufacturing networks. International Journal of Production Economics 131(1), 165-174. Download
  • Löhndorf N, Minner S. 2010. Optimal day-ahead bidding with renewable energies and storage. Energy Systems 1(1), 61-77. Download

Online Scenario Generator

Click here to generate scenarios from a multivariate distribution (normal, log-normal, uniform) for Monte Carlo simulation, numerical integration or stochastic programming. The generator is based on experiments with different scenario generation methods which are described in this article. The code is available on GitHub.


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