| Summary: | In the past very rudimentary methods have been used to plan and schedule the extraction of
ore from block cave operations. The basic assumption of these methods has been that the
movement of ore through the draw points is smooth and can be done in a specified sequence.
There are many operational research tools available to allocate resources and schedule
operations in an optimum way. Many of these were developed specifically for the
manufacturing and service industry. Although some optimization and scheduling tools have
been used in open pit mines, few have been applied in underground mining.
The principle of scheduling systems reviewed in this research is the link between strategic
goals and production scheduling. Two strategic goals in particular have been formulated in
this research the maximization of NPV and the optimization of the mine life in block caving.
Both of these goals have required the integration of geomechanical aspects of the ore
extraction, resource management, mining system and metallurgical parameters involved in
the mineral extraction.
One of the main results obtained in this thesis is the integration of mine reserves estimation
and development rate as a result of the production scheduling. Traditionally these parameters
have been computed independent of the production scheduling. The second contribution
made by this research was the formulation of link between the draw control factor and the
angle of draw. This relation was built in to the actual draw function to compute schedules
with high performance in draw control. Case studies have been used as an example of the
application of an operational research approach to scheduling in a block caving operation.
Future research has also been outlined to approach the problem of linking the short and the
long term planning, which has been resolved using mathematical programming tools. It is
proposed to integrate the actual reconciliation process into the scheduling so operational
information can be used to reproduce variability of the current long term planning models. === Applied Science, Faculty of === Mining Engineering, Keevil Institute of === Graduate
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