The planning of air cargo across an airlines' network is proven to be a complex problem consisting of multiple subproblems. Currently, all these subproblems are solved sequentially and by hand, resulting in partial solutions instead of an integrated solution. Two of these subproblems are the palletization of items onto ULDs and the scheduling of a workforce at a cargo terminal. This research proposes an implementable tool that tries to find a more integrated solution between these two subproblems. The tool provides loading instructions to ground handlers on how to combine cargo at the outstation. It makes use of both a dynamically determined volume threshold for ULDs containing cargo for one connecting dated flight (T-ULD) as a K-means clustering algorithm to combine cargo based on the connection time in the hub. A 1D Bin Packing Problem and Breakdown Scheduling model was created to study the effects of the new loading instructions. The dynamic threshold showed a predictable behaviour in the reduction of workload while achieving a baseline workload reduction of 1.4%. The tool further opens the opportunity to reduce workload even further with 24.9%, but this is associated with a risk and additional costs. The K-means clustering algorithm did not show an improvement to the baseline but it did offer the opportunity to cluster cargo based on multiple properties besides connection time at the hub.