Dynamic markets drive the need for more flexible and robust production systems. Agents are a means to cope with this new reality by providing dynamic scheduling functionalities. However, agents have rarely made the step from research labs to real production environments so far. To perform in these environments, the agents need to cope with real and therefore often imperfect information. This holds especially true for agent-systems performing integrated scheduling of production and transportation processes using real-time location information. In this paper, a modeling framework is proposed that is based on accuracy, granularity, and frequency as universal quality dimensions for information that should be incorporated in decision-making. These dimensions are applied within an agent-based system that is to perform integrated dynamic scheduling in a real manufacturing environment.