Resource Allocation for a Life Science Automation Line: a Petri nets Approach

High-throughput screening (HTS) processes have been increasingly used in the life sciences area for diverse bio-chemical experiments. This paper investigates the resource allocation issue of scheduling HTS processes under multiple constraints including operation constraints, resource constraints, starvation constraints and multi-experiment constraints. We first use timed transition Petri nets (PN) to model the HTS processes. We then illustrate the transition variant property of the PN model, and propose an efficient best-first (E-BF) algorithm combined with PN execution to find the schedule. Finally we apply the E-BF algorithm to three typical HTS experimental processes under different resource constraints and compare the E-BF algorithm with a traditional best-first (BF) algorithm as case studies. The results show that the E-BF algorithm can be used to allocate resources for HTS processes properly and can easily be more efficient than the BF algorithm when applied to transition variant PN.