Case Study: Using Software Optimization Techniques For The Optimal Allocation Of Equipment In A Mechanized Underground Mining Environment

A challenge in mine planning is the optimal allocation of resources to the appropriate workplaces within the shift cycle (labour, equipment type, and other resources) in order to carry out the shift cycle activities, achieve planned production targets, and ultimately maximize production within all infrastructure constraints. Generally, detailed mine planning is done on a monthly basis at a workplace level in order to determine the production targets. The production supervisor, mine overseer, or equivalent production manager must then make the appropriate resource allocation trade-off decisions on a daily basis per shift to try and deliver to the plan and required production target. Due to the complexity and number of permutations of possible resource allocation decisions, it is impossible for a human brain to make the appropriate and optimal allocation decisions that will result in achieving the maximum or target production. These allocation decision permutations must deal with the current status of workplaces (availability, position in the cycle etc.), the status of equipment availability (available, planned maintenance, or breakdown etc.), distance between the workplaces (traveling time of equipment between workplaces, etc.) and equipment type required for that shift cycle activity. All these factors need to be taken into account in resource allocation to plan for the shift, and even during the shift if a major piece of equipment becomes unavailable or a workplace activity is not progressing as planned. This paper is a case study on how an optimization algorithm can be used to create a production planning system for optimization of resource allocation in a mechanized underground platinum operation. A business case for optimal allocation of resources is demonstrated and compared to the current manual planned allocation.