Aiming at the problem that insufficient consideration of resource constraints in aircraft final assembly scheduling algorithms results in unexecutable plans, a resource weighted improvement algorithm is proposed, fully considering the spatial constraints, personnel qualification constraints, and the impact of resources on process fe asibility: Dynamic spatial competition constraints triggered by parallel processes were incorporated into traditional resource constraints to optimize the continuity of operational space; Diversified constraints on personnel qualifications were introduced to minimize labor redundancy and better align with real-world production requirements; And material–process–spatial coupled network (MPSCN) was innovatively constructed, in which the entropy weight method was employed to calculate resource weights in the process network, quantifying the impact of resources on process execution.With the objective of minimizing completion time, space and personnel constraints were embedded into the fitness functions of the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, process weights were integrated into the initial solution generation stage, leading to the development of the resource-weighted improved genetic algorithm (RW-IGA) and the resource-weighted improved particle swarm optimization (RW-IPSO).The experimental results show that RW-IGA reduces the average makespan by 9.26% compared to standard GA, while RWIPSO achieves a 1.62% reduction compared to standard PSO. As population size increases, the average optimization improvement rates of RW-IGA and RW-IPSO reach 1.32% and 2.03%, respectively. Among the four algorithms, RWIGA demonstrates the best optimization performance, achieving a maximum improvement of 15.42%.