The aircraft manufacturing industry is distinguished by its large-scale operations, high task volume, repetitive processes, and strict precision standards. To meet these demands, the integration of robotic systems for automated production is crucial. Given the customized requirements and small batch production typical in aircraft manufacturing, human-robot collaboration has emerged as a viable solution for drilling and riveting operations on aircraft fuselages. However, a critical challenge in these collaborative systems is the efficient allocation of tasks between humans and robots, ensuring that the overall system operates in a coordinated and optimized manner. This paper models the scenario of drilling tasks on aircraft fuselages using three methods. The first method is the Mixed Integer Linear Programming (MILP) approach, which has been thoroughly explored in the literature, with the goal of minimizing the overall completion time. The second method involves an extended version of the Hungarian algorithm, where each row’s coefficients are duplicated, and virtual tasks are added to convert the cost matrix into a square form. The third method is based on the Consensus-Based Bundle Algorithm (CBBA), which designs a bidding score function that better suits the characteristics and capabilities of heterogeneous agents, such as humans and robots. Simulations were carried out in Python, focusing on the drilling areas of the stringers and main frame. The results showed that the CBBA-based algorithm exhibited the lowest time complexity, while the MILP approach demonstrated the shortest computation time. Given the assigned task set, a greedy algorithm can be chosen for solving task execution planning for humans and robots.