The optimization of their business processes is a crucial challenge for many enterprises. This applies especially for organizations using complex cooperative information systems to support human work, production lines, or computing services. Optimizations can touch different aspects such as costs, throughput times, and quality. Nowadays, improvements in workflows are mostly achieved by restructuring the process model. However, in many applications there is a huge potential for optimizations during runtime as well. This holds particularly true for collaborative processes with critical activities, i.e., activities that require a high set up or changeover time, typically leading to waiting queues in instance processing. What is usually suggested in this situation is to bundle several instances in order to execute them as a batch. How the batching is achieved, however, has been only decided on static rules so far. In this paper, we feature Dynamic Instance Queuing (DIQ) as an approach towards clustering and batching instances based on the current conditions in the process, e.g., attribute values of the instances. Specifically, we extend our previous work on applying DIQ at single activities towards a queuing approach that spans activity sequences (DIQS). The approach is evaluated based on a real-world case study from the manufacturing domain. We discuss limitations and further applications of the DIQ idea, e.g., with respect to collaborative human tasks.

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