Aggregate models of detailed scheduling problems are needed to support aggregate decision making such as customer order acceptance. In this paper, we explore the performance of various aggregate models in a decentralized control setting in batch chemical manufacturing (no-wait job shops). Using simulation experiments based on data extracted from an industry application, we conclude that a linear regression based model outperforms a workload based model with regard to capacity utilization and the need for replanning at the decentralized level, specifically in situations with increased capacity utilization and/or a high variety in the job mix.