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This paper studies the multi-objective optimization of space station short-term mission planning(STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts properly, and arrange the devices reasonably. The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model is developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II(NSGA-II) is adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The proposed approach is demonstrated by using a test case with thirty-five missions, eighteen devices and three astronauts. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered. Moreover, through contrast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II(SPEA-II) and the Multi-objective Particle Swarm Optimization(MOPSO).
This paper studies the multi-objective optimization of space station short-term mission planning (STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II (NSGA-II ) adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered ast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II (SPEA-II) and the Multi-Objective Particle Swarm Optimization (MOPSO).