Mission-based clustering optimization model for maintenance force grouping and improved NSGA-Ⅱ solution algorithm

In response to the current issues with the organization of wartime equipment maintenance forces, a force organization method based on mission clustering analysis and an improved NSGA-Ⅱ algorithm is proposed. Based on the mission clustering analysis results obtained from the maintenance task clustering model, a multi-objective optimization model for maintenance force grouping was established with the objectives of minimizing the total maintenance time and the standard deviation of personnel workload. For the solution method of this multi-objective optimization model, the elite retention strategy and crossover operator of the traditional NSGA-Ⅱ algorithm were optimized and improved. The improved NSGA-Ⅱ algorithm was verified through the ZDT test function in terms of its superiority in convergence and solution set distribution. Using the maintenance mission of a certain artillery group as an example, simulation experiments and model algorithm analysis were conducted, resulting in a set of relatively ideal maintenance force organization schemes. This provides methodological and technical support for decision-makers to select schemes based on battlefield requirements and preference differences.