The truck allocation problem is an important section of the transportation system in open-pit mines. Most available models for the truck scheduling problem do not directly address the stochastic nature of truck-shovel systems by using multi-objective optimization techniques. This paper presents a chance-constrained goal programming (CCGP) model based on four important goals to estimate the impacts of the uncertainty on the efficiency of truck-shovel systems. The proposed model has been implemented using 11 schedule scenarios and different confidence levels (CLs) for loader’s production to determine the best allocation of trucks in an open-pit copper mine. The results display that the model can handle the quality and quantity of material required to achieve the objectives of the short-term production schedule of the mine in all CLs, even in the highest risk level. This model has a remarkable ability to meet the required objectives in terms of uncertainty in mining operations.

This paper deals with optimal decisions regarding truck and shovel scheduling problem in open-pit mines. A mixed-integer linear goal programming model (MILGP) is formulated for solving the problem, aiming at four major goals: (1) maximise production, (b) minimise deviations in head grade, (c) minimise deviations in tonnage to the ore destinations, and (d) minimise fuel consumption of mining trucks. This paper also examines the extent to which the priority of objectives can affect the results and efficiency of the mining operation. Implementation of the model with a copper mine case study demonstrated that the proposed model is effective and efficient.

This work deals with the optimal decisions for materials handling in open-pit mines. The main purpose of this research is to present a multi-stage approach, which has three sequential stages: (a) fleet size selection stage, (b) fleet allocation stage, and (c) real-time truck dispatching stage. At first, a simulation-based optimisation method is provided to evaluate the optimal number of trucks. Then, a new heuristic algorithm is developed for making operational decisions. To evaluate the performance of the proposed framework, the dispatching algorithm used by Modular Mining DISPATCH is developed. The results indicate that the proposed method yields fairly good performance.

The present study aims to propose new strategies based on mixed-integer non-linear programming (MINLP) models for the equipment sizing (ES) problem to verify the overall efficiency of the fleet. The developed models estimate the optimal size of trucks concerning the match factor value with two different strategies. The first strategy deals with each loader type, and the second one is applied simultaneously with all types of loaders. The proposed approaches are compared to a simulation strategy to assess the models. Implementing models with a copper mine case study provides a more efficient haul fleet size than the decisions offered by the simulation method. Moreover, the presented strategies provide an effective way to improve equipment performance where the current mine strategy does not adapt well. A key contribution of this research is the development, implementation, and verification of new optimization and simulation methods to address the ES problem in open-pit mines.

Truck and shovel operations comprise approximately 60% of the total operating costs in open pit mines. In order to increase productivity and reduce the cost of mining operations, it is essential to manage the equipment used with high efficiency. In this work, the chance-constrained goal programing (CCGP) model presented by Michalakopoulos and Panagiotou is developed to determine an optimal truck allocation plan in open pit mines and reduce the waiting times of trucks and shovels. The developed goal programming (GP) model is established considering four desired goals: “maximizing shovel production”, “minimizing deviations in head grade”, “minimizing deviations in tonnage feed to the processing plants from the desired feed” and “minimizing truck operating costs”. To employ the developed model, a software is prepared in Visual Studio with C# programming language. In this computer program, the CPLEX optimizer software is incorporated for solving the developed goal programing model. The case study of Sungun copper mine is also considered to evaluate the presented GP model and prepared software. The results obtained indicate that the developed model increases the mine production above 20.6% with respect to the traditional truck allocation plan, while meeting the desired grade and the stripping ratio constraints.