The buildup phase at KLM Cargo requires cumbersome planning actions because of a slow data handover at the warehouse and an uncertain initial state of the buildup process at the beginning of the shift. This study presents a model to improve scheduling of future buildup shifts according to flight departure times of KLM. The most important objectives are to minimize the delays and equally distribute the workload. For this purpose, a MILP model has been created. The problem is modeled as a scheduling problem with time windows. Next to the branch-and-bound method, an adapted tabu search is implemented as an alternative optimization technique. Both methods show adequate performance in terms of computation time and model convergence. With the help of a real-time data architecture, scheduling can now be standardized, take less time, incorporate more detailed information and in addition, bottlenecks can be identified early.