Truck allocation model using linear programming and queueing theory

Abstract

Truck-Shovel is the most common means used for transportation of ore/waste in surface mining operations, but it is the most costly unit procedure in a truck-shovel mining system. The present advancement in computing technology offers the potential of refining truck-shovel productivity and consequent savings. Introducing a truck allocation model in a mine can attain operational improvements by decreasing waiting times and achieve other benefits through enhanced optimal routing and grade control. The efficiency of the working truck-shovel fleet is determined by the allocation model in use, the complexity of the truck shovel system and a multiplicity of other variables. In maximum cases computer simulation is the most applicable and operational method of comparing the different allocation approaches.
A model is presented here to minimize the number of trucks allocated to a set of shovels, considering throughput and ore grade constraints. A nonlinear relation between a shovel’s throughput and the number of trucks allocated to the shovel using queueing theory and linear programming is being established. It is assumed that each shovel is allocated a single truck size. Different linear programming methods are being suggested to optimize the constraints.