Robust strategies for hierarchical production planning
For this project a situation typical for automotive suppliers will be used as a test case. The entire planning process from master planning down to the shop floor execution will be investigated, based on a stochastic environment (including market, machines, and workers), and taking into account simultaneously the two divergent objectives costs and service level. For that purpose various hierarchical planning strategies will be implemented and tested using a specially developed simulation environment. Special attention will be paid to the selection and optimization of the planning parameters. In order to obtain a better estimation of the planning strategies’ potential, optimization models for a global planning problem will be developed and their solutions (exact ones for simplified scenarios, good approximations for more complex situations) will be compared to the hierarchical planning strategies with respect to their efficiency. Furthermore, analytical approaches will be developed in order to validate the empirical results, at least for simpler test instances, and to enhance the understanding of the dependencies between a strategy’s parameter values and performance indicators (e.g. costs and service level). The overall goal is to identify robust planning strategies for different production situations.
This study will generate a set of rules for choosing suitable planning methods and parameters depending on the prevailing environmental conditions. Additionally, validity ranges for these choices can be used to determine when a change of planning strategy becomes necessary. This project will deliver an essential contribution to the improvement of the coordination of planning levels in a stochastic environment and represents an important step towards the application of the research results to real-world production planning problems. In the mid-term, the project may help to improve the planning stability of production planning systems used in practice.
FWF Translational Research
Das Projekt wird im Translational Research Programm durch den FWF Wissenschaftsfonds gefördert.