Open Pit Mine Production Scheduling with Stockpiling Using Constraint Programming

The Open Pit Mine Production Scheduling Problem (OPMPSP) is a complex optimization problem that seeks to determine the best sequence for extracting mineralized material from the ground in order to maximize the Net Present Value (NPV). This process must take into account various capacity constraints, such as mining rates, processing limits, stockpiling capacities, blending requirements, and precedence constraints, which dictate the order in which blocks can be extracted. Traditionally, the OPMPSP has been formulated as a mixed integer programming problem, employing binary and continuous variables to represent the extraction of each block. However, this approach has limitations, particularly when it comes to addressing the stockpiling issue. In response, some researchers have proposed mathematical models, which offer a more accurate representation of the problem but come with their own set of challenges. In this study, an alternative approach to tackle the OPMPSP with stockpiling by employing constraint programming (CP) is explored. The method was carefully tested on the Newman1 dataset from the well-known Minelib library. The results show that the CP-based model obtained a production schedule having about 2.69 % higher NPV than the NPV of the solution, which is recorded in the Minelib Library.