求解动态维修资源优化调度的多目标进化算法

A multi-objective evolutionary algorithm for optimal scheduling of dynamic maintenance resources

为解决维修资源调度过程中出现的维修资源预测不准、资源冲突的问题,本文建立了不同作战阶段的多供应中心-多需求点的的动态维修资源优化调度模型,使得多个供应中心可以及时、高效地对需求点进行维修资源调度,减少了资源调度时间和每个需求点的维修资源不满足量。为了更好地求解提出的模型,本文提出了一种改进的多目标进化算法,在经典的多目标进化算法的基础上,使用正态分布交叉算子、全局探索增强型差分进化算子和自适应变异算子的协同进化策略,提高了算法的局部搜索能力和种群的多样性。仿真实验表明,本文提出的算法具有良好的收敛性和分布均匀性,并且具有较高的求解效率。

In order to solve the problems of inaccurate prediction and resource conflict in the process of maintenance resource scheduling,this paper establishes a dynamic maintenance resource optimization scheduling model of multi supply centers and multi demand points in different combat stages,so that multiple supply centers can schedule maintenance resources at demand points in time and efficiently,It reduces the resource scheduling time and the unsatisfied amount of maintenance resources at each demand point.In order to better solve the proposed model,an improved multi-objective evolutionary algorithm is proposed.Based on the classical MOEA/D algorithm,the co evolution strategy of normal distribution crossover operator,global exploration enhanced differential evolution operator and adaptive mutation operator is used to improve the local search ability and population diversity of the algorithm.Simulation results show that the proposed algorithm has good convergence and distribution uniformity,and has high solution efficiency.