Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry
Planning an equipment fleet is a complex engineering challenge involving (1) the capacity selection of equipment pieces forming a fleet, (2) the determination of fleet size, (3) strategic timing of equipment acquisitions, and (4) ensuring compatibility between interconnected equipment. Selecting equ...
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| Published in: | Mining technology (2018) Vol. 134; no. 2; pp. 143 - 158 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London, England
SAGE Publications
01.06.2025
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| Subjects: | |
| ISSN: | 2572-6668, 2572-6676 |
| Online Access: | Get full text |
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| Summary: | Planning an equipment fleet is a complex engineering challenge involving (1) the capacity selection of equipment pieces forming a fleet, (2) the determination of fleet size, (3) strategic timing of equipment acquisitions, and (4) ensuring compatibility between interconnected equipment. Selecting equipment type involves evaluating capacity to meet production requirements effectively while considering operational constraints. It also requires determining the optimal number of units to avoid underutilisation or redundancy. In multi-period projects, the timing of equipment acquisition further adds complexity, as decisions must align with production rates that evolve over time. Moreover, matching equipment types is essential to ensure smooth and efficient workflows. This study develops a dynamic multi-period mixed-integer non-linear programming model to optimise equipment selection, considering capital recovery, operating costs, and equipment availability under match factor and production constraints. The model's effectiveness is demonstrated through a case study encompassing greenfield and brownfield scenarios in open-pit mining operations. The greenfield scenario emphasises phased equipment acquisition to align with production ramp-up and minimising initial costs. The brownfield scenario addresses the integration of aging and new equipment to sustain operational efficiency. The results highlight the model's flexibility and applicability, offering a robust tool for optimising equipment selection while balancing cost and performance. |
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| ISSN: | 2572-6668 2572-6676 |
| DOI: | 10.1177/25726668251348712 |