Optimization of Two Echelon Equipment Spare Parts InventoryAffected by Demand Correlation

doi:10.3969/j.issn.1007-7375.2019.02.008

Abstract

Abstract: The multi-echelon inventory model of spare parts is usually based on the assumption that spare parts demand is independent of each other, but with the increase of inventory system level and the application of collaborative management, the correlation of spare parts demand will significantly affect the inventory optimization decision. Aiming at the problem of spare parts inventory where demands are relevant, a two-echelon inventory decision model for spare parts is established with the constraint of service response time and the goal of minimizing the inventory cost and shortage cost. The Nataf probability transforming is applied to construct the random samples which meet designated correlation condition and probability distribution from obtained marginal probability density function, and the Monte Carlo simulation and genetic algorithm are combined to solve the optimal inventory allocation scheme. Simulation result shows that, the optimal decisions of spare parts inventory change with the increase of correlation coefficient of demand. Adjusting the inventory decision appropriately according to the changes of demand correlation will help reduce the total cost of spare parts inventory system and improve the responsiveness of the inventory system to customer needs.

Key words: spare parts inventory, demand correlation, Nataf transforming, Monte Carlo simulation, genetic algorithm

CLC Number:

TJ761.1
F224

LUO Wei, FU Zhuo, DONG Wei. Optimization of Two Echelon Equipment Spare Parts InventoryAffected by Demand Correlation[J]. Industrial Engineering Journal , 2019, 22(2): 57-66.

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