An milp approach to multi-location, multi-period equipment selection for surface mining with case studies
|dc.identifier.citation||Burt, C. and Caccetta, L. and Fouché, L. and Welgama, P. 2016. An milp approach to multi-location, multi-period equipment selection for surface mining with case studies. Journal of Industrial and Management Optimization. 12 (2): pp. 403-430.|
In the surface mining industry, the Equipment Selection Problem involves choosing an appropriate eet of trucks and loaders such that the longterm mine plan can be satisfied. An important characteristic for multi-location (multi-location and multi-dumpsite) mines is that the underlying problem is a multi-commodity ow problem. The problem is therefore at least as dificult as the fixed-charge, capacitated multi-commodity ow problem. For long-term schedules it is useful to consider both the purchase and salvage of the equipment, since equipment may be superseded, and there is the possibility of used pre-existing equipment. This may also lead to heterogeneous eets and arising compatibility considerations. In this paper, we consider two case studies provided by our industry partner. We develop a mixed-integer linear programming model for heterogeneous equipment selection in a surface mine with multiple locations and a multiple period schedule. Encoded in the solution is an allocation scheme in addition to a purchase and salvage policy. We develop a solution approach, including variable preprocessing, to tackle this large-scale problem. We illustrate the computational effectiveness of the resulting model on the two case studies for large sets of equipment and long-term schedule scenarios.
|dc.publisher||American Institute of Mathematical Sciences (A I M S Press)|
|dc.title||An milp approach to multi-location, multi-period equipment selection for surface mining with case studies|
|dcterms.source.title||Journal of Industrial and Management Optimization|
|curtin.department||Department of Mathematics and Statistics|
|curtin.accessStatus||Open access via publisher|
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