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%.