Uncertain optimization model for multi-echelon spare parts supply system

The optimization of spare parts inventory for equipment system is becoming a dominant support strategy, especially in the defense industry. Tremendous researches have been made to achieve optimal support performance of the supply system. However, the lack of statistical data brings limitations to these optimization models which are based on probability theory. In this paper, personal belief degree is adopted to compensate the data deficiency, and the uncertainty theory is employed to characterize uncertainty arising from subjective personal cognition. A base-depot support system is taken into consideration in the presence of uncertainty, supplying repairable spare parts for equipment system. With some constraints such as costs and supply availability, the minimal expected backorder model and the minimal backorder rate model will be presented based on uncertain measure. Genetic algorithm is adopted in this paper to search for optimal solution. Finally, a numerical example is employed to illustrate the feasibility of the optimization models.