Underground mine scheduling is a complex process characterised by formidable decision combinations, conflicting goals and constraint interactions. New mine scheduling models that incorporate these factors offer powerful tools for identifying and quantifying performance improvements in underground mining operations. Operations research (OR) models are well suited to this style of problem and were selected as the basis for the proposed underground mine scheduling models.
The objective of this dissertation was to develop and demonstrate new OR models for underground mine scheduling. This objective was achieved by:
• reviewing OR concepts and techniques,
• reviewing existing underground mine scheduling techniques,
• proposing a model of the underground mine scheduling process,
• defining the underground mine scheduling problem,
• formulating a mixed integer programming optimisation model and a linear programming based heuristic model for the multiple period production scheduling problem,
• adapting existing integer programming optimisation and priority based heuristic models to the multiple period activity scheduling problem,
• applying the proposed models to data sets from two underground base metal mines, and,
• evaluating schedule results from the two case studies.
Case study results demonstrated that the new models were capable of generating comparable or better schedules than those developed using existing scheduling practice. In particular, the production scheduling models indicated that significant NPV improvements could be obtained by a more thorough analysis of stoping options. The value of a well-defined scheduling process for maintaining current and consistent data sets was emphasised. It is anticipated that these models will assist planning and decision making in underground mines by allowing the evaluation of a greater number of alternative strategies and by providing a benchmark against which other scheduling models can be evaluated.
Recommendations for further investigations are provided with emphasis on improved solution quality for the heuristic models and reduced solution times for the optimisation models.