Optimization of Concurrent Testing Processes in Space Launches Based on Genetic Algorithms

The increasing frequency of space launch missions underscores their vital role in national security and technological advancement. These missions involve a complex set of tasks, each reliant on limited resources and subject to various constraints. Traditionally, mission planning has depended on the expertise of personnel, leading to potential biases and inefficiencies. Thus, there is a critical need for automated methods that facilitate objective and rapid task planning, minimizing human error. Modeling the task planning for these missions poses significant challenges due to the vast search space and multiple resource constraints. As the complexity of tasks and their inter-dependencies increase, finding optimal solutions becomes more difficult. To address this, this paper introduces a dynamic resource intelligent scheduling model that incorporates executable task lists, resource constraints, and the progress of ongoing tasks. Utilizing advanced optimization techniques such as genetic algorithms, simulated annealing, and particle swarm optimization, our model effectively identifies solutions consistent with manual calculations. This innovative approach enhances task scheduling efficiency and ensures optimal outcomes, marking a significant advancement in space launch mission planning.