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dc.contributor.authorBurt, Christina Naomi
dc.contributor.supervisorProf. Louis Caccetta

In the surface mining industry the equipment selection problem involves choosing a fleet of trucks and loaders that have the capacity to move the materials specified in the mine plan. The optimisation problem is to select these fleets such that the overall cost of materials handling is minimised. The scale of operations is such that although a single machine may cost several million dollars to purchase, the cost of operation outweighs this expense over several years. This motivates the need for a purchase and salvage policy, so that the optimal equipment replacement cycle can be achieved.Mining schedules often appear with multiple mining locations and dump-sites, where a dump-site can also represent a stockpile or a mill. Multiple periods must also be considered, which adds to the complexity of determining the optimal replacement policy for equipment. Further, some mines begin with a pre-existing set of equipment, and the subsequent fleet must be both compatible and satisfy the mill constraints. We also need to consider the possibility of a heterogeneous fleet.The equipment selection problem is cursed with a cascade of inter-dependent variables and parameters. For example, the cost of operating a piece of equipment depends on its utilisation; the utilisation depends on the availability of the equipment; and the availability depends on the age of the equipment. We formally define the equipment selection problem in the Introduction (Chapter 1) and further discuss the complexities of the problem.While numerous methods from Operations Research and Artificial Intelligence have been applied to this problem, optimal multiple period solutions remain elusive. Also, pre-existing equipment and heterogeneous fleets have largely been ignored. We present a comprehensive literature review in Chapter 2, outlining the methods applied and candidly discussing the successes and pitfalls of these approaches. We also organise the literature by linking related fields, such as Shovel-Truck Productivity and Mining Method Selection.In Chapter 3 we extend the match factor ratio, an important productivity index for the mining industry. Previously this ratio was restricted to homogeneous fleets and single location/dump. We provide several alternative ratios that incorporate heterogeneous trucks, heterogeneous loaders and multiple locations. These extensions are then applied to solutions in subsequent chapters to indicate the efficiency of the selected fleets in terms of the proportion of time they are working (rather than waiting).In this thesis, we consider the equipment selection as an optimisation problem. We wish to purchase only whole units of trucks and loaders, which suggests integer variables are appropriate for this problem. Similarly, salvage occurs in whole units. As the productivity constraints (satisfying the mill requirements) are linear, we consider an integer programming approach.In Chapter 4 we present a single location/dump multi-period integer program that provides a purchase and salvage policy for a surface mine. We demonstrate through a retrospective case study that the solutions are economically better than current methods. We also demonstrate the robustness of the model through a series of test cases. We extend this model to a mixed integer linear program (MILP) to optimise over multiple locations/dump-sites in Chapter 5, and test this model on two case studies. This model also produces an optimised allocation policy for the multiple mining locations and truck routes.In Chapter 6 we consider the utilisation of the equipment in the objective function. This MILP model provides the purchase and salvage policy for a single-location multiperiodsurface mine. In this model we introduce constraints that capture the non-uniform piecewise linear ageing of the equipment. We test this model on a case study used in previous chapters.All of the presented models allow for pre-existing equipment and heterogeneousfleets. Further, they all consider multiple period schedules, ensuring they are all innovative equipment selection tools.

dc.publisherCurtin University
dc.subjectmaterials handling
dc.subjectoptimisation approach
dc.subjecteet of trucks and loaders
dc.subjectsurface mines
dc.titleAn optimisation approach to materials handling in surface mines
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusOpen access

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