A hybrid augmented Lagrangian multiplier method for the open pit mines long-term production scheduling problem optimization

To improve the quality of decision making in the mining operation, it is essential to find global optimum in problems with large dimensional scales. It is widely accepted that Long-term production scheduling (LTPS) problem is playing key role in mining projects to improve their performance by considering availability constraints while maximizing the project total profits during the period. Since production scheduling problems are NP-hard, there is need of improving scheduling methodologies to get good solution. This paper presents a hybrid model between augmented Lagrangian relaxation (ALR) and Genetic algorithm (GA) to solve the LTPS problem. We propose to apply the ALR method on the LTPS problem which to improve its performance speeding up the convergence and also, GA is used to update the Lagrangian multipliers. The results from case study show that the ALR method is effective in solving large-scale problem and generation a feasible solution then the traditional linearization method. Furthermore, the proposed hybrid strategy based on GA showed better performance in comparison to the available methods.