Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/2310
Title: Longterm schedule optimization of an underground mine under geotechnical and ventilation constraints using SOT
Authors: Sharma, Vijay
Keywords: Mine Schedule;Schedule Optimization;Schedule Optimization Tool (SOT);Genetic Algorithm
Issue Date: 26-Jan-2015
Publisher: Laurentian University of Sudbury
Abstract: Long-term mine scheduling is complex as well time and labour intensive. Yet in the mainstream of the mining industry, there is no computing program for schedule optimization and, in consequence, schedules are still created manually. The objective of this study was to compare a base case schedule generated with the Enhanced Production Scheduler (EPS®) and an optimized schedule generated with the Schedule Optimization Tool (SOT). The intent of having an optimized schedule is to improve the project value for underground mines. This study shows that SOT generates mine schedules that improve the Net Present Value (NPV) associated with orebody extraction. It does so by means of systematically and automatically exploring the options to vary the sequence and timing of mine activities, subject to constraints. First, a conventional scheduling method (EPS®) was adopted to identify a schedule of mining activities that satisfied basic sets of constraints, including physical adjacencies of mining activities and operational resource capacity. Additional constraint scenarios explored were geotechnical and ventilation, which negatively effect development rates. Next, the automated SOT procedure was applied to determine whether the schedules could be improved upon. It was demonstrated that SOT permitted the rapid re-assessment of project value when new constraint scenarios were applied. This study showed that the automated schedule optimization added value to the project every time it was applied. In addition, the reoptimizing and re-evaluating was quickly achieved. Therefore, the tool used in this research produced more optimized schedules than those produced using conventional scheduling methods.
URI: https://zone.biblio.laurentian.ca/dspace/handle/10219/2310
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Master's Theses

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