RCPN

RCPN – how should one configure production capacities over time so that future demands can be handled in the best possible way?

RCPN – Robust Configuration of Production Networks

Project Leader: Prof. Dr. Jörg Rambau
Contact: Prof. Dr. Jörg Rambau

Project start: 2016/02/22

Project team: External partners:
  • Dipl.-math. Emanuel Moser, Karlsruhe Institute of Technology



Project description:

Application Background

RCPN

Whenever a company want to be prepared to accept a large order, capacities in its production plants have to be in place beforehand. But ususally one does not know exactly what the future opportunities will be. And capacities that are not utilized produce more costs than anything else. Therefore, it is necessary to plan the capacity expansion or reduction of production facilities such that the company is prepared for the most important/most likely future scenarios. In this project, the Lehrstuhl für Wirtschaftsmathematik (Prof. Dr. Jörg Rambau) cooperates with the Institut für Produktionstechnik in Karlsruhe.

Contribution to the Mission of MODUS

We plan to develop a model that captures the delay in the procurement of production capacities. Our approach will employ stochastic mixed-integer linear programming models from the realm of stochastic capacity expansion. This approach takes profit from the fact that capacity expansion and reduction decisions have a delayed effect. The method essentially turns the multi-stage dynamics (when do we change which capacity and how shall we react to demand) into a two-stage dynamics (what expansion/reduction path do we follow, and how shall we react to demand sequences). To this end, the capacity paths are enumerated and represented by indicator variables; the demand response is represented by continuous variables that specify which capacity contributes to the satisfaction of which demand.

Results

So far, we have made up our minds to try first the stochastic two-stage capacity expansion models.


University of Bayreuth -