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Showing 2 results for Akbari

A.d. Akbari, M. Osanloo , M.a. Shirazi ,
Volume 19, Issue 5 (IJES 2008)
Abstract

Planning and design procedure of an open pit mining project just can be started after ultimate pit determination. In the carried out study in this paper it was shown that the most important factor in ultimate pit determination and in consequence in the whole planning and design procedure of an open pit mine is the metal price. Metal price fluctuations in recent years were exaggerated and imposed a high degree of uncertainty to the mine planning procedure while none of the existent algorithms of the pit limit determination consider the metal price uncertainty. Real Option Approach (ROA) is an efficient method of decision making in the condition of uncertainty. This approach usually used for evaluation of defined natural resources projects up to now. This study considering the metal price uncertainty used real option approach to prepare a methodology for determining the Ultimate Pit Limits (UPL). The study was carried out on a non-ferrous metallic cylindrical ore deposit but the achieved methodology can be adjusted for all kinds of the deposits. The achieved methodology was comprehensively described through the examples in a way that can be used by the mine planners.


Hossein Akbaripour, Ellips Masehian,
Volume 24, Issue 2 (IJIEPR 2013)
Abstract

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.

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