Multi-stage mine production timetabling with optimising the sizes of mining operations: an application of parallel-machine flow shop scheduling with lot streaming

Liu, Shi Qiang; Kozan, Erhan; Masoud, Mahmoud; Li, Debiao; Luo, Kai

doi:10.1007/s10479-022-05134-z

Cited by 1 publication

(1 citation statement)

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“…The scheduling of underground mining activities is influenced by a range of factors, including the availability of equipment, geological conditions, and safety considerations. The efficient scheduling of mining requires the use of advanced optimization techniques to develop optimal schedules that minimize operational costs while maximizing productivity [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Scheduling Model for Underground Metal Mines under Equipment Failure Conditions

Jia

Wang

et al. 2023

Sustainability

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Equipment failure is a common problem in mining operations, resulting in significant delays and reductions in production efficiency. To address this problem, this paper proposes a dynamic scheduling model for underground metal mines under equipment failure conditions. The model aims to minimize the impact of equipment failures on production operations while avoiding extensive equipment changes. A case study of the southeastern mining area of the Chambishi Copper Mine is presented to demonstrate the effectiveness of the proposed model. The initial plan was generated using the multi-equipment task assignment model for the horizontal stripe pre-cut mining method. After equipment breakdown, the proposed model was used to reschedule the initial plan. Then, a comparative analysis was carried out. The results show that the proposed model effectively reduces the impact of equipment failures on production operations and improves overall mining execution at a low management cost. In general, the proposed model can assist schedulers in allocating equipment, coping with the disturbing effects of equipment failure, and improving mine production efficiency.

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Section: Introductionmentioning

confidence: 99%