[Zur Zeit nur in englischer Sprache verfügbar]
Optimal Lead Time Expediting Policies
Most inventory models assume that lead times are exogenously given, i.e., that orders arrive after a lead time that cannot be altered after the order is placed. However, in many situations inventory managers have the option to expedite open orders. We incorporate the expediting option into common inventory models and solve these models optimally and computationally efficient. Numerical results show that expediting policies can achieve high cost savings compared to inventory policies without expediting options.
Stochastic Supply Chain Design
It is desirable to design a supply chain network that is flexible enough to react to changing customer needs but also aims to minimize cost. Special challenges lie in the uncertainty of future demand and cost as well as in the consideration of realistic cost structures, e.g. economies of scale. We develop mathematical models that capture this uncertainty as well as the non-linearity of cost. Since the resulting optimization problems are very hard to solve, we also investigate special solution approaches to find solutions of high quality within reasonable time.
Value of Real Time Yield Information
The random yield problem is an important issue in many procurement, production, and assembly sitations. In a production process, the exact quantity produced (yield) can be random, as can the exact quantity received in a shipment process. Recent developments in order tracking and data transmission allow to access real time yield information (YI). We develop optimal and heuristic policies that take advantage of YI. The results are reduced inventory cost and accurate investment decisions in information technology which enables YI.
Stochastic Optimization in Power Systems
Rising prices for fossil fuels and political rulings currently cause immense challenges to power generation. As a result, additional supply uncertainty arises, by e.g. intermittent power sources. This creates complex stochastic decision problems, for which models as well as efficient solution algorithms are not currently available. By developing Monte Carlo-based optimization algorithms we provide solution approaches to large-scale stochastic multi-stage decision problems.
Segmented Delivery Lead Times in the Automotive Industry
Buying a car means either buying the car that’s currently on stock or waiting a considerable amount of time and getting the car with exactly the equipment and color you want. In online retailing however, we are long used to choose between overnight express delivery and standard delivery – why not in the automotive industry? In our research we investigate which implications such segmented delivery lead times would have for an automotive supply chain. We take a close look at the effect on supplier capacities and inventory, differentiation strategies in distribution, etc. in order to determine expected cost as well as analyze the reliability of lead times.