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Hybrid Algorithms for Redesigning MRP

3-year research project in cooperation with the University of Applied Sciences Upper Austria, funded by the Austrian Science Fund (FWF).

Material Requirements Planning (or MRP for short) concerns medium-term production planning within the widely applied hierarchical production planning framework. It used to determine which items are needed at what due date, when to start their production, and what amount to produce. In spite of its popularity in practice, traditional MRP suffers from a number of problems: machine capacity is assumed to be infinite, demand is assumed to be deterministic, production and replenishment lead times are assumed to be constant (and independent of current system load), and rolling horizon planning aspects are ignored.

The overall aim of this project is to develop efficient modeling and solution approaches to overcome all of these drawbacks and to  provide (close to) optimal mid-term production plans. A main focus is the rolling horizon planning aspect in combination with stochastic demand and capacitated production system behavior and the integration of simulation based and optimization based approaches.

Research Project

Hybrid Algorithms for Redesigning MRP

Funding Agency

Austrian Science Fund (FWF)


October 1, 2020 - September 30, 2023

Project lead

Klaus Altendorfer
Sophie Parragh




  • Schlenkrich M., Parragh S.N. (2022) Separating setup and quantity decisions in stochastic lot sizing models, OR 2022, Karlsruhe, Germany. 07.09.2022.
  • Schlenkrich M., Parragh S.N. (2022) The impact of risk aversion and flexibility on stochastic and robust lot sizing decisions, EURO 2022, Espoo, Finnland, 04.07.2022.
  • Schlenkrich M., Parragh S.N. (2022)  Computational analysis of stochastic and robust optimization models for capacitated lot sizing under uncertain customer demand, Manufacturing and Service Operations Management Conference 2022, Munich, Germany, 27.06.2022
  • Schlenkrich M., Parragh S.N. (2021) Robust optimization models for MRP – a comparison to stochastic programming approaches and a decomposition based solution method, OR 2021, Bern/online, 02.09.2021