Reliability-Driven Multiechelon Inventory Optimization With Applications to Service Spare Parts for Wind Turbines

There are multiple warehouses in a multiechelon inventory system, and the size of the state space increases exponentially with the number of warehouses. Therefore, the curse of dimensionality becomes unavoidable when performing steady-state analysis. Most existing studies calculate the inventory cost or supply chain reliability based on specific assumptions. For example, it often assumes that the lead time is either zero or an integral multiple of the review period, and that each warehouse adopts a base-stock policy. This article considers a more practical and prevalent situation where the lead time is less than a review period, and a more general (s, S) strategy is adopted. The curse of dimensionality during steady-state analysis is alleviated by decomposing transition probabilities. Then, the cost and supply chain reliability are derived from steady-state distributions. Finally, a case study involving spare part inventory of wind turbines is considered. Nondominated inventory strategies are obtained using the particle swarm optimization method to strike a balance between costs for the wind turbine manufacturer and wind farm owners.

Automated summary

In a multiechelon inventory system, the curse of dimensionality is inevitable due to the exponential increase in state space. This article addresses this issue by decomposing transition probabilities and considering a more practical situation where the lead time is less than the review period. It also provides a case study on spare part inventory for wind turbines, obtaining nondominated inventory strategies to balance costs for the manufacturer and owners.