Production Capacity Insurance Considering Reliability, Availability, and Maintainability Analysis

Abstract

Production scheduling is one of the most substantial indicators in achieving short-term, medium-term, and long-term production planning goals. Satisfying this indicator can remarkably reduce total costs and enhance profitability. The haulage system significantly affects the mine production scheduling. Indeed, the uncertain nature of the haulage system causes considerable differences between the nominal and actual haulage production capacity. Thus, it is necessary to predict the haulage fleet size considering various variables to maintain production scheduling and reach nominal production capacity. Although the haulage fleet size plays a crucial role in satisfying medium-term production planning goals, it is often determined under steady-state conditions without quantifying and analyzing uncertain variables such as reliability, availability, and maintainability (RAM). In this study, a simulation optimization algorithm is developed to determine the most optimal fleet requirements considering influential factors such as RAM analysis, production scheduling, material flow rate, and stochastic environmental and operational phenomena. The proposed methodology addresses the haulage fleet size at one stage by developing a parallel combination of mixed-integer programming and discrete-event simulation. This approach has been validated using a haulage fleet operation at the Sungun mine. In this case, the analysis procedure is accomplished by determining the optimal haulage fleet size with and without considering RAM during the mining operation. Then, all mining zones are assessed based on the number of failures and loss of production capacity. During 1,500 operation hours, the lost production capacity in Mining Zones 1, 2, and 3 were 346,000, 319,000, and 481,900 t, respectively. Also, a sensitivity analysis is conducted to identify the impact of the failure and downtime of each subsystem of the haul trucks on the haulage production capacity. Thus, the engine subsystem was the most critical truck’s subsystem. The findings showed the desired performance of the proposed methodology.