Self-Optimization in Adaptive Logistics Networks
摘要
The spectrum of applications for AI is extremely broad, ranging from support for complex decisions and data-driven corporate strategies to the automation of everyday processes. In logistics networks, a plurality of decisions are to be made on a daily basis. Typically, those decisions are comprised of a combination of forecasting and optimization, forming the area of prescriptive analytics. In this chapter, we present two use cases for arriving at optimal decisions: the case of prescribing cost-optimal order policies for the stocking of spare parts, and the case of mixing raw materials to final products with varying raw material quality.