Discrete-event simulation of mine equipment systems combined with a reliability assessment model based on genetic algorithms

Abstract

The combination of simulation with the maintenance analysis of mining equipment has been proven to be an effective tool to assess the impact of equipment failures on mining equipment. Genetic algorithms have been applied to multiple areas of mine design, mostly involving optimization solutions. With regard to maintenance analysis, past research in mining focused on the design of a genetic algorithm based modelling technique that is applied to the failure data of equipment to assess the reliability of a machine under study. The objective of this research is to develop, integrate and demonstrate that a methodology involving the combination of a reliability assessment model based on genetic algorithms with a discrete-event simulation model can be an effective tool for maintenance analysis of mining equipment. The reliability assessment model based on genetic algorithms provides input in the form of times between failures (TBFs) to a discrete-event simulation model. The simulation component emulates a typical mine development cycle to analyse the effect of load-haul-dump (LHD) equipment failures on production throughput, mechanical availability and equipment utilization. Furthermore, two equivalent simulation models, built in AutoMod and Simul8, are compared to evaluate the merits of employing one simulation software package over the other. This final component of the research offers the opportunity to assess two different simulation tools for the same mining problem.