These software algorithms work with variables as route length, loading mime, hauling time under certain parameters of ore quality and quantity. Based over this information they optimize the variables and assign more or less trucks to a certain face regardless of the truck (as independent unit) performance. Truck Performance We can define the truck performance as the capability of the truck to haul material between two or more points efficiently using all their own mechanical functions and characteristics.
In order to understand this concept we should notice that two trucks, with the same characteristics and age, can have different reduction rates over the same route; that difference in their production rate should be address to some factors as their maintenance history (which affect their reliability), accidents and drivers ability. Moreover, these differences could be bigger if we have a fleet composed by trucks from different models and makers. Objectives The objective of this research project is to improve the haul efficiency and reducing cost by develop a tool that can be a complement for the Dispatch@or Jigsaw@ software .
This tool will select the best trucks, base in their performance, mongo the fleet in order to be assigned to the active faces. Methodology Based in the huge amount of information registered by the Dispatch@ original@ software in the mine data warehouse is possible to use this information to find performance patterns, that help us to predict the outcome of some situations and giving us the possibility to analyze and change them. In order to achieve this, the data should be select and process from the data warehouse using data mining tools. The steps in the process to develop this tool are : 1.
Data acquisition : The data will be acquire using data mining from Dispatch@ earwigs@ database. The data to be collected should describe the code of the truck (model, maker, mine ID) and haul time from any load face to any destination (e. G. Crusher, stockpile,dumpster). 2. Database develop : A new data base has to be develop which associate to each truck a time for a “route”. The number of “routes” will depend of the number of load faces and destinations. Once the “routes” are defined the time that a truck needs to complete a “route” can be calculated from the values in the main database using statistics.
This time alee will be assigned to the “route” and to the truck. 3. Data processing : Using ANN and based upon production objectives for each load face as output we can determine the best truck combination to be assign to each load face. Evaluation The project objectives has been achieved if we use less trucks to reach the production quotes, which means a reduction in the hauling cost and increase of efficiency . In order to improve this tool it has to be capable to be updated also, as a future development we can mix the performance and cost information per truck and use it to assign trucks.
