Joint Optimization of Spare Parts and Condition Based Maintenance Using Monte Carlo Approach

Today according to the competitive conditions, companies try to reduce their inventory level to diminish the costs. In companies inventory management and control should establish a balance between inventory level of spare parts, cost and the risk that arising from absence of part when is needed. It is clear that this level of spare parts inventory affected by facility characteristics and its reliabilities. As a whole in the various industries, maintenance policies and determination of spare parts inventory level individually performed. In this paper is used Monte Carlo simulation to combinatorial optimize the condition based maintenance activities and spare parts inventory. Used binary genetic algorithm to find the optimal values of decision variables. In this optimize policy inspection intervals (T), maximum of spare parts inventory(S), spare parts renewed order point(s), frontier erosion to perform preventive replacement(LP) and human resource required to performing corrective and preventive replacements(labor). In the case study for maintenance events simulation used the oil analysis database of a construction company that included about 8000 data of oil analysis. Intended Monte Carlo simulation and also genetic algorithm coded in Matlab Software and finally the result of simulation is brought.