Optimal Reordering Strategy for Three-Echelon Spare-Parts Inventory Systems Under Disruption-Dependent Lead-Time Uncertainty: Application to Wind Energy Systems

Background: The rapid expansion of wind energy systems has increased the need for reliable and cost-effective maintenance logistics, where the availability of critical spare parts is essential for sustaining turbine performance and reducing downtime. From this background, this study develops a three-echelon spare-parts inventory optimization model for wind-energy maintenance systems under disruption-dependent lead-time uncertainty. The model considers a hierarchical supply structure, consisting of a central warehouse, regional hub, and local maintenance base, where replenishment lead times are represented as a mixture of normal operating conditions and disruption-induced delays. Method: A total average cost function is formulated by incorporating ordering, holding, shortage, disruption penalty, and downtime costs, and a hybrid Nested Enumeration–Bisection Algorithm is developed as a method to determine the optimal order quantity, reorder points, and shipment multipliers. Results: Numerical experiments based on wind-turbine maintenance scenarios show that disrupted-condition lead-time distributions substantially affect total system cost. More variable disruption distributions increase total average cost, whereas more stable distributions produce lower-cost and more balanced inventory policies. Conclusions: The findings indicate that explicitly modeling disruption-sensitive lead times can improve spare-parts planning and provide decision support for enhancing cost efficiency and reliability in multi-echelon wind-energy maintenance logistics.