Application of network flow and zero-one programming to open pit mine design problems.

Persistent Link:
http://hdl.handle.net/10150/184797
Title:
Application of network flow and zero-one programming to open pit mine design problems.
Author:
Cai, Wenlong.
Issue Date:
1989
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An algorithm which adopts a moving cone approach but is guided by maximal network flow principles is developed. This study argues that from a network flow point of view, the re-allocation problem is a major obstacle to prevent a simulation oriented pit design algorithm from reaching the optimum solution. A simulation oriented pit design algorithm can not resolve the re-allocation problem entirely without explicit definition of predecessors and successors. In order to preserve the advantages of moving cone algorithm and to improve the moving cone algorithm, the new algorithm trys to avoid the re-allocation situations. Theoretical proof indicates that the new algorithm can consistently generate higher profit than the popular moving cone algorithm. A case study indicates that the new algorithm improved over the moving cone algorithm (1% more profit). Also, the difference between the new algorithm and the rigorous Lerchs-Grossmann algorithm in terms of generated profit is very insignificant (0.015% less). The new algorithm is only 2.08 times slower than the extremely fast moving cone algorithm. This study also presents a multi-period 0-1 programming mine sequencing model. Once pushbacks are generated and the materials between a series of cutoffs are available for each bench of every pushback, the model can quickly answer, period by period, what is the best (maximum or minimum) that can be expected on any one of these four items: mineral contents, ore tonnages, waste tonnages and stripping ratios. This answer is based on a selected cutoff and considers the production capacity defined by the ore tonnage, the desired stripping ratio and the precedence constraints among benches and pushbacks. The maximization of mineral contents is suggested to be the direct mine sequencing objective when it is permissible. Suggestions also are provided on how to reduce the number of decision variables and how to reduce the number of precedence constraints. A case study reveals that the model is fast and operational. The maximization of mineral contents increases the average grades in early planning periods.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Strip mining.; Mines and mineral resources -- Design -- Data processing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Mining and Geological Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Kim, Young C.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleApplication of network flow and zero-one programming to open pit mine design problems.en_US
dc.creatorCai, Wenlong.en_US
dc.contributor.authorCai, Wenlong.en_US
dc.date.issued1989en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn algorithm which adopts a moving cone approach but is guided by maximal network flow principles is developed. This study argues that from a network flow point of view, the re-allocation problem is a major obstacle to prevent a simulation oriented pit design algorithm from reaching the optimum solution. A simulation oriented pit design algorithm can not resolve the re-allocation problem entirely without explicit definition of predecessors and successors. In order to preserve the advantages of moving cone algorithm and to improve the moving cone algorithm, the new algorithm trys to avoid the re-allocation situations. Theoretical proof indicates that the new algorithm can consistently generate higher profit than the popular moving cone algorithm. A case study indicates that the new algorithm improved over the moving cone algorithm (1% more profit). Also, the difference between the new algorithm and the rigorous Lerchs-Grossmann algorithm in terms of generated profit is very insignificant (0.015% less). The new algorithm is only 2.08 times slower than the extremely fast moving cone algorithm. This study also presents a multi-period 0-1 programming mine sequencing model. Once pushbacks are generated and the materials between a series of cutoffs are available for each bench of every pushback, the model can quickly answer, period by period, what is the best (maximum or minimum) that can be expected on any one of these four items: mineral contents, ore tonnages, waste tonnages and stripping ratios. This answer is based on a selected cutoff and considers the production capacity defined by the ore tonnage, the desired stripping ratio and the precedence constraints among benches and pushbacks. The maximization of mineral contents is suggested to be the direct mine sequencing objective when it is permissible. Suggestions also are provided on how to reduce the number of decision variables and how to reduce the number of precedence constraints. A case study reveals that the model is fast and operational. The maximization of mineral contents increases the average grades in early planning periods.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectStrip mining.en_US
dc.subjectMines and mineral resources -- Design -- Data processing.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineMining and Geological Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorKim, Young C.en_US
dc.contributor.committeememberDaemen, Jaak J. K.en_US
dc.contributor.committeememberDenny, John L.en_US
dc.contributor.committeememberHarris, DeVerle P.en_US
dc.contributor.committeememberMyers, Donald E.en_US
dc.identifier.proquest9003479en_US
dc.identifier.oclc703255628en_US
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