Prof. Dr. Nils Löhndorf

Associate professor

Discipline(s) Production, distribution & supply chain management / Quantitative methods in economics & management
Sujets de recherche Operations research, in particular optimization under uncertainty, with current applications in operational management and valuation of energy storage systems
Faculté ou Centre Faculté de Droit, d'Économie et de Finance
Department Département Sciences économiques et gestion
Adresse postale Campus Kirchberg, Université du Luxembourg
6, rue Richard Coudenhove-Kalergi
L-1359 Luxembourg
Bureau sur le campus B11B
E-mail
Téléphone (+352) 46 66 44 6893

Nils Löhndorf is Chair Holder in Digital Procurement and Associate Professor at the Luxembourg Centre for Logistics and Supply Chain Management (LCL).

Nils's research interests are in operations research, in particular optimization under uncertainty, with current applications in operational management and valuation of energy storage systems.

Nils has published his research in leading journals like Operations Research, European Journal of Operational Research, and IIE Transacations.

Energy companies in Austria, Germany, and Italy actively use Nils's models for operation and valuation of energy storage systems, like reservoirs of hydropower plants and natural gas storages, as well as virtual power plants.

Prior to joining LCL, Nils was an assistant professor at Vienna University of Economics and Business, Austria, where he received his Habilitation in Business Administration in 2017.

Last updated on: mardi 13 décembre 2022

Nils vision is to develop models that help decision-makers to make better decisions in the face of uncertainty and to foster the use of stochastic-dynamic optimization in practice. Current research projects are:

  • optimal dynamic hedging of natural gas storage contracts when markets are incomplete;
  • optimal bidding strategies for storages and flexible capacity in short-term markets for electricity;
  • strategic capacity planning of production networks when there is substantial uncertainty about long-term sales forecast;
  • empirical analysis of methods for multistage stochastic programming, in particular approximate dynamic programming and scenario-tree based stochastic programming.



Last updated on: 25 mai 2018