Application of Fuzzy Multiple Attribute Decision Making in Mining Operations

Kesimal, Ayhan; Başçetin, Ataç

doi:10.1142/s0950609802000835

Cited by 22 publications

(8 citation statements)

References 11 publications

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“…FIS have been applied with success in mining (Kesimal & Bascetin, 2002;Taboada, Ordóñez, Saavedra, & Fiestras-Janeiro, 2006), mineral processing (Chuk, Ciribeni, & Gutierrez, 2005) mineral exploration (Luoa & Dimitrakopoulos, 2003) and other fields of mineral industry (Meech, 2006). Their success is mainly due to their similarity to human perception and reasoning, and their intuitive handling and simplicity, which are important factors for the acceptance and usability of the mining systems.…”

Section: Fuzzy Inference Systems (Fis)mentioning

confidence: 99%

Selective mining of multiple-layer lignite deposits. A fuzzy approach

Galetakis

Vasiliou

2010

Expert Systems with Applications

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Section: Fuzzy Inference Systems (Fis)mentioning

confidence: 99%

Selective mining of multiple-layer lignite deposits. A fuzzy approach

Galetakis

Vasiliou

2010

Expert Systems with Applications

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“…Currently, such decision making has to be made based on limited and highly suboptimal cost estimates. There has been some research on the applications of fuzzy set theory to help improve this process [17], but this is no substitute for knowing the true costs associated with optimal designs. Another factor underlining the importance of optimization is the rapid advance in mining technology, particularly mining automation (see, e.g., [14]).…”

Section: Underground Mine Designmentioning

confidence: 99%

Network optimization for the design of underground mines

Brazil

Thomas

2006

Networks

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Efficient methods to model and optimize the design ofopen-cut mines have been known for many years. The design of the infrastructure of underground mines has a similar potential for optimization and strategic planning. In this article we discuss the use of network optimization to tackle this problem. The idea is to design a connected system of declines, ramps, drives, and possibly shafts, to minimize capital development and haulage costs over the lifetime of a mine. This can be modeled as a variation on the Steiner problem, with suitable metric and constraints. These constraints include: an upper bound on the absolute gradient of arcs in the embedded network (typically 1/7), turning circle restrictions for navigability, and obstacle avoidance. Here we give an overview of the literature, focussing on our published work. We investigate the way in which this design problem can be modeled as a network optimization problem that accurately reflects the real costs involved while remaining mathematically tractable. Our approach is to first establish a fundamental model, which principally captures the development costs of the mine, and to study its geometric properties. We then outline more complicated generalized models, which add extra costs and constraints to the fundamental model but are still solvable.

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“…Bandopadhyay (1987) indicates the potential for the use of fuzzy logic in the selection of an appropriate system of operation. Kesimal & Bascetin (2002) have authored a study using applications of the fuzzy approach to multi-criteria decision-making in mining practice. Whittle and Borzorgebrahimi (2004) tested the set theory for a hybrid pits model, where the pits model, derived from Lerchs-Grossmann algorithm and conditional simulation were linked.…”

Section: Introductionmentioning

confidence: 99%

Archives of Mining Sciences

Krzak

Panajew

2019

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The basis for a mineral deposit delimitation is a qualitative and quantitative assessment of deposit parameters, qualifying a deposit as an economically valuable object. A conventional approach to the mineral deposit recognition and a deposit detailed parameters qualification in the initial stages of development work in the KGHM were presented in the paper. The goals of such recognition were defined, which through a gradual detailed expansion, resulting from the information inflow, allows for the construction of a more complete decision-making model. The description of the deposit parameters proposed in the article in the context of fuzzy logic, enables a presentation of imprecise statements and data, which may be a complement to a traditional description. Selected non-adjustable and adjustable s-norm and t-norm operators were demonstrated. Operators effects were tested for selected ore quality parameters (copper content and deposit thickness) by constructing adequate membership functions. In a practical application, the use of chosen fuzzy logic operators is proposed for the assessment of the qualitative parameters of copper-silver ore in the exploitation blocks for one of the mines belonging to KGHM Polish Copper S.A. The considerations have been extended by including the possibility of using compensation operators.

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Application of Fuzzy Multiple Attribute Decision Making in Mining Operations

Cited by 22 publications

References 11 publications

Selective mining of multiple-layer lignite deposits. A fuzzy approach

Selective mining of multiple-layer lignite deposits. A fuzzy approach

Network optimization for the design of underground mines

Archives of Mining Sciences

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