Cross-docking is a warehousing strategy that (ideally) moves goods from inbound docks directly to outbound docks. In reality, goods often need to be temporarily stored. Cross-docking is typically set up as a block-stacking warehouse (BSW), where goods are stored directly on the ground. Autonomous mobile robots (AMRs) could significantly reduce BSW costs. To deploy AMR systems to BSWs, five interlaced decision problems, including the storage location assignment problem (SLAP), need to be solved. Because of the combinatorial complexity of BSWs, andtheabsenceofpertinentusecasedataandfittingsimulationsoftware, this is a challenging task. This work seeks to alleviate these gaps by (1) extending SLAPStack, a fine-grained open-source BSW simulation framework to accommodate cross-docking, (2) providing CROSSStacks, a real-world cross-docking dataset, and (3) evaluating two dual command cycle SLAP strategies as of yet untested for BSWs. One of the approaches outperforms a naive cross-docking SLAP strategy.