A Network Decomposition Approach for Approximating the Steady-State Behavior of Markovian Multi-Echelon Reparable Item Inventory Systems
提出一种网络分解方法，将大型多级可修件库存系统分解为重叠的子网络模型，通过迭代求解近似稳态概率，数值实验表明该方法准确且高效。
We develop a method for obtaining approximate steady-state probabilities for large multi-echelon reparable item inventory systems modeled as non-Jacksonian Markovian networks with finite state space. The approximation involves decomposing the network model into smaller overlapping local subnetwork models, solving them in “isolation” and iterating back and forth among the subnetwork models until convergence is obtained. Numerical results show that the method is quite accurate and efficient for this application.