Multi-objective Optimization Model for Multi-echelon Spare Parts Supply System Under uncertain circulation

The optimization of spare parts inventory for equipment support 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 grounded on probability theory. And, the spare parts inventory optimization is aimed at obtaining optimal military and economic benefits. These goals often conflict with each other, and meantime they are also restricted with each other. This is exactly the embodiment of the characteristics for multi-objective optimization problem. 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. With some goals such as costs and backorders, the multi-objective expected value model will be presented based on uncertain measure. Multi-objective Genetic algorithm is adopted in this paper to search for optimal solution. Finally, we will employ a numerical example to clarify the possibility of the optimization models. Through this paper, we can get a new model to control inventory of spare parts, and we can obtain optimal military and economic benefits.