A hierarchical algorithm involving two-layer optimization-based control policies with varying degrees of abstraction is proposed, including upper layer task scheduling and lower layer local path planning. A scenario with two robot arms performing cooperative pick-and-place tasks for moving objects is specifically addressed. The main focus of the paper lies on the bottom layer of the hierarchical control scheme, more precisely on the online generation of the synchronous robot trajectories using distributed minimum-time model predictive control (DMPC) algorithms. To this end, we introduce a decelerating coupling term in the cost functions of the individual distributed optimization algorithms to synchronize the overall robot motion. The performance of the algorithm is illustrated by extensive simulations with high-fidelity robot dynamic models.