Abstract
In this paper, a fuzzy programming model, incorporating fuzzy measures of costs and ore reserves, is developed to evaluate different design alternatives in the context of the selection of the underground mine development system. The bauxite deposit is usually mined using the sublevel mining method. This method extracts the ore via sublevels, which are developed in the ore body at regular vertical spacing. In such an environment, we consider the development system as a weighted network interconnecting all sublevels with surface breakout point using the minimum cost of development and haulages. Selection of the optimal development system is based on the application of Convex Index and composite rank. The uncertainties related to the future states of transportation costs are modeled with a special stochastic process, the Geometric Brownian Motion. The results indicate that this model can be applied for solving underground mine development problems.
Highlights
The investment environment associated with the mining industry is unique when compared with the environment encountered by typical manufacturing industries
The selection of an underground mine development system is classified as a strategic decision-making process, which has the most influence on the future of a mine
We consider the development system as a network interconnecting all sublevels with surface breakout point, using the minimum costs of development and haulage
Summary
The investment environment associated with the mining industry is unique when compared with the environment encountered by typical manufacturing industries. There are three main development systems to gain access to an ore body: vertical shaft, decline (ramp) and adit. These three systems can be mutually combined, and in that, the number of potential alternatives is increased. Every section of the network is weighted by an adequate fuzzy cost function, which combines the cost needed to build up the section and the cost of ore transportation along it The values of this function are changed over the project time, using a stochastic process, Geometric Brownian Motion, to simulate them. By evaluating the networks spanned from each sublevel, it can be seen how depth, sublevel ore reserves and fixed production rate affect the efficiency of the development system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
