Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming

Y. Li, Erkan Topal, S. Ramazan

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

A mine landform progression plan can provide a clear outlook of the entire mining operation. To produce such an output requires detailed placement schedule of the mined material, including the volume (or tonnage) and the allocated dumping location. However, current practise mainly focuses on the ore production, over-simplifying the waste material scheduling. As a result, a rock dump is often treated as a single point in long term planning, making it difficult to predict the progression pattern over the life of mine. Without such a guidance, it is almost impossible to carry out progressive rehabilitation of the waste rock dumps. The lack of dumping schedule could cause delay in development construction, i.e., tailing storage facility (TSF) and ROM-pad. Other downstream effect due to the over-simplification is inaccurate estimation of required truck hours, which could have huge financial impact on the operation. In this paper, mixed integer programming (MIP) models of different objective functions, i.e., maximise truck productivity by minimising the overall haulage distance, minimise required truck deviation between adjacent years, and a hybrid between the two objectives, are utilised to generate the long term optimum rock placement schedules under the criteria of satisfying site specific conditions. All three MIP models are implemented in a large scale open pit mine. The numerical solutions from the models forms three different rock placement schedules, based on which, the yearly truck requirements are easily calculated and compared. The graphical results show the three corresponding landform progression patterns over the life of mine, providing the optimised long term forecast of the operation.

Original languageEnglish
Title of host publicationAdvances in Applied Strategic Mine Planning
PublisherSpringer International Publishing
Pages669-686
Number of pages18
ISBN (Electronic)9783319693200
ISBN (Print)9783319693194
DOIs
Publication statusPublished - Jan 17 2018

Fingerprint

Landforms
open pit mine
Mixed Integer Programming
Integer programming
Progression
Trucks
landform
Schedule
Rocks
Placement
Term
rock
Programming Model
haulage
ROM
Rehabilitation
Tailings
Inaccurate
Patient rehabilitation
tailings

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Engineering(all)
  • Mathematics(all)
  • Computer Science(all)

Cite this

Li, Y., Topal, E., & Ramazan, S. (2018). Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming. In Advances in Applied Strategic Mine Planning (pp. 669-686). Springer International Publishing. https://doi.org/10.1007/978-3-319-69320-0_39

Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming. / Li, Y.; Topal, Erkan; Ramazan, S.

Advances in Applied Strategic Mine Planning. Springer International Publishing, 2018. p. 669-686.

Research output: Chapter in Book/Report/Conference proceedingChapter

Li, Y, Topal, E & Ramazan, S 2018, Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming. in Advances in Applied Strategic Mine Planning. Springer International Publishing, pp. 669-686. https://doi.org/10.1007/978-3-319-69320-0_39
Li Y, Topal E, Ramazan S. Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming. In Advances in Applied Strategic Mine Planning. Springer International Publishing. 2018. p. 669-686 https://doi.org/10.1007/978-3-319-69320-0_39
Li, Y. ; Topal, Erkan ; Ramazan, S. / Optimising the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed integer programming. Advances in Applied Strategic Mine Planning. Springer International Publishing, 2018. pp. 669-686
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