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Abstract In the mining industry, truck assignment is an important and complex process and an optimal truck allocation can result in significant savings. In this paper, a truck allocation model is formulated using a chance-constrained, stochastic optimization approach that can accommodate uncertain parameters such as truckload and cycle time. A real-time hauling framework, which consists of the chance-constrained optimization model and a model updater, is developed to compensate for changes in the uncertain key operating parameters. The use of the model updater helps the truck allocation system to adapt to random operational changes. The effectiveness of the chance-constrained approach in dealing with uncertain process parameters, when coupled with model updating, is shown to be a viable implementation framework in the dispatching operation.
Ta et al. (Thu,) studied this question.