Optimization of shovel-truck system for surface mining

Introduction In a surface mining operation, a materials handling system is composed of loading, hauling and dumping subsystems. Effective and efficient materials handling systems can be developed only through a detailed consideration of these subsystems in a systems analysis framework. The transport of material from production faces to dumping sites is accomplished by rail, truck, belt conveyor or hydraulic transport. Shovel-truck systems are most common in open pit mining. Two available techniques to analyse these systems, linear programming and queuing models, are used and compared in this study. The most important factor in every operation is profitability. Productivity of equipment used is an important factor of profitability. Profitability can be increased by optimization of the equipment combination used. Therefore the first goal in these models is to maximize productivity and hence increase production, which in turn will result in cost reduction. Studies conducted for the truck allocation were carried out by several authors. Muduli and Yegulalp (1999) studied the modelling truck-shovel systems as a closed queuing network with multiple job classes. Soumis et al. (1989) discussed the evaluation of the new truck dispatching in the Mount Wright mine using linear programming. Sgurev et al. (2003) studied an automated system for realtime control of the industrial truck haulage in open-pit mines. Alarie and Gamache (2002) studied the overview of solution strategies used in truck dispatching systems for open pit mines. Nenonen et al. (1981) used the interactive computer model for truck/shovel operations in an open pit mine; Ramani (1990) studied the haulage system simulation analysis in surface mining. Barnes et. al. (1972) studied the probability techniques for analysing open pit production systems. Carmichael (1986) applied cyclic queuing theory to determine the production of open-cut mining operations, and Koenigsberg (1982) used in his study some concepts of queuing theory. Shangyao et al. (2008) developed an integrated model that combines ready mixed concrete (RMC) production scheduling and truck dispatching in the same framework. Sabah et al. (2003) present a methodology based on the queuing theory, which is incorporated in a computer module to account for the uncertainties that are normally associated with the equipment selection process.