基于动态进化算法的多阶段备件供应优化决策

Multi-stage spare parts supply optimization based on dynamic evolutionary algorithm

由于实际备件保障工作中备件需求以间歇性需求为主,备件供应通常为多阶段的动态优化。针对以上问题,构建了多阶段备件供应数学模型。为求解动态优化模型,提出了一种元启发式动态进化算法。首先,在经典差分进化算法中增加了环境变化检测算子和环境变化响应策略,使得差分进化算法能够解决环境变化的动态优化问题。其次,提出了自适应莱维飞行策略,使得算法在环境发生变化时仍能保持良好的全局搜索能力和局部寻优能力。算例表明,所提出的动态自适应差分算法能够求得模型的最优可行解,且算法的分布性和收敛性均得到了很大的提升。

Since the spare parts demand is almost the intermittent demand in real spare parts support,the supply of spare parts is usually a multi-stage dynamic optimization problem.Focusing on this,a multi-stage mathematical model of spare parts supply is constructed.In order to solve this kind of dynamic optimization problem,a meta-heuristic dynamic optimization algorithm is proposed.Firstly,an environment change detector and an environment change response strategy are added to the classical differential evolution algorithm,which enables the differential evolution algorithm to solve the dynamic optimization problem when the environment changes.Secondly,a self-adaptive Levy flight strategy is proposed,which enables the algorithm to maintain a good global exploration and local exploitation capability when the environment changes.Empirical test shows that the proposed dynamic self-adaptive difference algorithm can obtain the optimal feasible solution of the model,and the distribution and convergence of the algorithm are greatly improved.