Analysing equipment allocation through queuing theory and Monte-Carlo simulations in surface mining operations

Abstract

Shovels and trucks are widely used in earth moving and surface mining operations as a materials handling system. Insufficient equipment allocation for a given fleet results in not achieving production targets, high production costs and opportunity costs associated with shovel idle times or truck queues. Match factor is commonly used to measure the compatibility among trucks and shovels in terms of fleet size, truck cycle and shovel loading times. The calculated match factor is a deterministic value and does not reflect the sensitivities to unexpected variations of cycle, loading and waiting times. In this paper, the effects of uncertainties associated with shovel loading, truck waiting times, truck cycle times and fleet availability on match factor are assessed. In doing so, queuing theory is applied to model the waiting times for trucks, and Monte-Carlo samplings are used to model fleet availability, shovel waiting and truck cycle times. The proposed approach is demonstrated through a case study.