Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty

This work considers the strategic flexibility and capacity planning under uncertain demands in production networks of automobile manufacturers. We present a deterministic and a stochastic model, which extend existing approaches, especially by an anticipation scheme for tactical workforce planning. This scheme is compared to an extended formulation of the deterministic model, which incorporates workforce planning via detailed shift models. The stochastic model is efficiently solved by an accelerated decomposition approach. The solution approach is integrated into a decision support system, which calculates minimum–cost product allocations and capacity plans. Our numerical results show that, in spite of the considerably increased complexity, our approach can efficiently handle hundreds of scenarios. Finally, we present an industrial case study.