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

被引:6
作者
Yan, Bin [1 ]
Zhou, Yifan [1 ]
Zhang, Mofan [2 ]
Li, Zhaojun [3 ]
机构
[1] Southeast Univ, Sch Mech Engn, Nanjing 211189, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China
[3] Western New England Univ, Dept Ind Engn & Engn Management, Springfield, MA 01119 USA
基金
中国国家自然科学基金;
关键词
Costs; Reliability; Supply chains; Wind farms; Wind turbines; Optimization; Markov processes; Conditional probability; inventory optimization; lead time; multiechelon; reliability-driven; SHIPMENT CONSOLIDATION; SYSTEM; MODEL;
D O I
10.1109/TR.2022.3178596
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:748 / 758
页数:11
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