Application of Life Cycle Cost Techniques for Equipment Selection in Surface Mines

摘要

This paper addresses equipment selection for surface mines. Given a mine plan, the ultimate objective is to select the trucks and loaders such that the overall cost of materials handling is minimized. Such a fleet must be robust enough to cope with the dynamic nature of mining operations where the production schedule can sometimes be dependent on refinery requirements and demand. Due to the scale of operations in mining, even a small improvement in operation efficiency translates to substantial savings over the life of the mine. There is a considerable amount of literature concerning shovel-truck productivity for construction equipment selection and shovel-truck equipment selection for surface mines. Although a variety of modelling methods have been applied, such as Queuing Theory, Bunching Theory, Linear Programming and Genetic Algorithms, the solutions obtained are consistently inadequate. In the mining industry current methods use spreadsheets and are heavily dependent on the expertise of a specialist consultant. Classical Methods include concepts such as match factor, bunching theory and productivity curves. These methods often rely on brute force to achieve a feasible solution, where a handful of truck types may be enumerated by hand for the minimum cost fleet size. Operations Research techniques such as Integer Programming (IP) and Nonlinear Programming have been applied in a bid to achieve an optimal solution. Current IP solutions tend to oversimplify the model or rely on excessive assumptions. More complex constraints can be included in these formulations, which help to describe a more realistic idea of the performance of a particular fleet. Artificial Intelligence techniques such as expert systems, knowledge based methods and genetic algorithms have been applied to equipment selection with some success, although optimality has not been demonstrated in the literature. Common weaknesses amongst all of these models are fleet homogeneity, loader (or truck) type pre-selection and restricted number of passes (from loader to truck). Fleet homogeneity assumes that the truck fleet should only consist of one type of truck. Yet there is no reason to believe that a mixed-type fleet underperforms a homogeneous-type fleet. Loader (or truck) type pre-selection requires a highly skilled and experienced engineer to select a loader type based on geographical and geological information. This can be a time consuming task and a demonstration of optimality is unlikely. Although there is a general preference for restricting the maximum passes from loader to truck, there is also no evidence in literature to support this constraint. The equipment type selection should occur alongside fleet size selection if a bid at optimality is desired. Some research has modeled the equipment replacement problem but focuses on replacement time rather than optimizing the type and number of trucks/loaders replacements. This paper provides an approach to one of the various methods of equipment planning for a surface mine in a given geo-mining conditions