Abstract

This paper discussed the problem of a multi-echelon and multi-period joint strategy of supply and inventory. Considering the characteristics of the (s, S) supply strategy, we determined the supply time and calculated the quantity of each customer’s spare parts supply consumption in the lead time. Then the count and the cost of spare parts consumption can be obtained by analyzing the spare parts supply process. The joint mathematical model is established to find the optimal supply scheme for each period with the goal of the minimum total cost. In this case, the optimal allocation scheme is obtained by the improved particle swarm optimization (PSO) in adjusting the self-adaptive weight, penalty function, coding process. This paper mainly analyzed the condition adaptability of (s, S) policy by adjusting parameters to achieve more rightness supply and inventory joint optimization strategy.

Abstract

In the modern industry, in order to reduce the inventory pressure, a variety of parts began to use unified kind spare parts for maintenance. However, highly integrated equipment is more difficult to use traditional RCM models, and researchers begin to steering based on state monitoring methods. Deepen a prediction of equipment failure. This paper mainly discussed the data-driven analysis method based on the Wiener process to predict the fault law of the same type. The joint model innovatively adopts the (''s''-1, ''s'') policy considering the industrial characteristic and multi-period resupply. In the end, we analyze (''s''-1, ''s'') policy in joint optimization by comparison to draw the optimal policy combination.