Abstract
Material handling in surface mines accounts for around 50% of the operational cost. Optimum truck dispatching plays a critical role in the reduction of this operational cost in truck and shovel surface mines. Researchers in this field have presented several mathematical models to solve the truck dispatching problem optimally. However, a critical survey of the literature has shown that three significant drawbacks exist in the available truck dispatching models. The published models underestimate the importance of the interaction between truck fleet, shovel fleet, and the processing plants. They also disregard goals set by strategic-level plans. Moreover, none of the available models account for the uncertainty associated with the input parameters. In this paper we present a new truck dispatching model that covers all of these drawbacks, using a fuzzy linear programming method. The performance of the developed model was evaluated through implementatin in an active surface mining operation. The results show a significant improvement in production and fleet utilization.
Highlights
As mining operations involve expenditures of millions or billions of dollars, cutting their costs by even two or three per cent will result in considerable savings for the stakeholders
One of the several existing ways to improve the performance of material handling systems in truck and shovel surface mining operations is to make optimal decisions for truck allocation and dispatching
We present a decision-making model that makes optimal decisions for truck dispatching in truck and shovel surface mines
Summary
As mining operations involve expenditures of millions or billions of dollars, cutting their costs by even two or three per cent will result in considerable savings for the stakeholders. Improving the performance of the material handling system and subsequently reducing its operating costs would result in significant savings. One of the several existing ways to improve the performance of material handling systems in truck and shovel surface mining operations is to make optimal decisions for truck allocation and dispatching. We present a decision-making model that makes optimal decisions for truck dispatching in truck and shovel surface mines. Several models to solve the truck dispatching problem have been developed since the 1970s. The models published far have three limitations, which lead to non-optimal dispatching of trucks. The existing truck dispatching models usually omit the truck fleet, shovel fleet, or the processing plant from their calculations. This paper aims to introduce a new mathematical model to solve the truck dispatching problem in surface mines. We implemented our model in a real case study, using the study’s site’s in-place fleet management system, which is the backbone algorithm of Modular Mining DISPATCH® (Modular Mining Systems Inc., 2020), as the benchmark to evaluate the model performance
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