No abstract

No abstract

Uncertainty is an inherent aspect of the open-pit mine production scheduling problem (MPSP); however, little is reported in the literature about solution methods for the stochastic versions of the problem. In this paper, two variants of a variable neighbourhood descent algorithm are proposed for solving the MPSP with metal uncertainty. The proposed methods are tested and compared on actual large-scale instances, and very good solutions, with an average deviation of less than 3% from optimality, are obtained within a few minutes up to a few hours.

No abstract

Production scheduling of open-pit mines is an important problem arising in surface mine planning as it determines the raw materials to be produced yearly over the life of the mine, assesses the value of the mine, and contributes to the sustainable utilization of mineral resources. Finding the optimal schedule is a complex task, involving large data sets and multiple constraints. This paper introduces a two-phase hybrid solution method. The first phase relies on solving a series of linear programming problems to generate an initial solution. In the second phase, a variable neighborhood descent procedure is applied to improve the solution. Upper bounds provided by CPLEX are used to evaluate the efficiency of the proposed method. Its performance is also assessed by comparing it to recent solution methods proposed in the literature and to an alternate method implemented in commercial mine planning software commonly used by professional mine planners. The results of these computational experiments indicate the efficiency of the proposed method and its superiority over the other methods. It finds excellent solutions (within less than 3.2 % of optimality on average) for large instances of the problem in a few seconds up to a few minutes. It also provides new best-known solutions for benchmark instances from the literature, and it can solve instances recently-published algorithms have found intractable.