Solving a large SIP model for production scheduling at a gold mine with multiple processing streams and uncertain geology

Abstract

One of the main steps during the decision-making process of long-term mine planning is the definition of the optimal sequence of extraction, which usually is synonymous of maximizing the discounted cash flow of the project subjected to several constraints arising from aspects of technical, physical, and economic limits. The Open-Pit Mine Production Scheduling (OPMPS) comprises several intricacies related to its size and uncertainty of input parameters. Due to its complexity and prohibitive size, traditional mine planning usually relies on heuristic or meta- heuristic methodologies which are able to provide good solutions in a reasonable amount of time. However, most of the uncertainty that surrounds the mining complex is ignored leading to non-realistic results. In this paper, a new heuristic approach is explored in order to solve a stochastic version of the OPMPS problem accounting for geological uncertainty in terms of metal content, multiple processing streams, and stockpiling option. The methodology involves generating an initial solution by solving a series of sub-problems and this initial solution is improved using a network-flow based algorithm. The algorithm was applied to a relatively large gold deposit with more than 119 thousands blocks. Results have shown that the methodology is promising to deal with large-size mine instances in reasonable time.