Branch-and-price is a common approach to optimally solve time-extended multi-robot task allocation (MRTA) problems. However, so far no complex dependencies could be considered. Complex dependencies arise if multiple decompositions of tasks are available that can be allocated to different robots. We propose three different methods how to handle complex dependencies within branch-and-price (BnP) approaches for MRTA problems. The decomposition method is based on a decompose-then-allocate concept, while the decision variable method and the cluster method incorporate the complex dependencies explicitly into the overall optimization model. An evaluation is conducted to compare the proposed approaches.