Adaptive Membership Selection Criteria using Genetic Algorithms for Fuzzy Centroid Localizations in Wireless Sensor Network

This paper investigates the effect of fuzzy inputs, i.e., signal strength, of various known nodes, to fuzzy logic systems in order to derive a proper weight for Centroid, properly used to approximate the location in wireless sensor networks with its key advantage on simplicity but with precision trade-off. Due to a fluctuation behavior of location estimation precisions with respect to a diversity of various inputs, here, we propose the use of heuristic approach applying genetic algorithms with mutation and cross-over steps to adaptively seek the optimal solution – a proper number of membership functions for fuzzy logic systems in weighted Centroid – to achieve higher location estimation accuracy. The performance of our methodology is effectively confirmed by the intensive evaluation on a large scale simulation in various topologies and node densities against fixed membership function scenarios including a traditional Centroi

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.The authors wish to thank FCT for the financial support under the doctoral Grant SFRH/BD/71501/2010, as well as the construction company that kindly provided the real-world data. Also, we wish to thank Olaf Mersmann for kindly providing the R code for the SMS-EMOA algorithm

Longterm schedule optimization of an underground mine under geotechnical and ventilation constraints using SOT

Long-term mine scheduling is complex as well time and labour intensive. Yet in the mainstream of the mining industry, there is no computing program for schedule optimization and, in consequence, schedules are still created manually. The objective of this study was to compare a base case schedule generated with the Enhanced Production Scheduler (EPS®) and an optimized schedule generated with the Schedule Optimization Tool (SOT). The intent of having an optimized schedule is to improve the project value for underground mines. This study shows that SOT generates mine schedules that improve the Net Present Value (NPV) associated with orebody extraction. It does so by means of systematically and automatically exploring the options to vary the sequence and timing of mine activities, subject to constraints. First, a conventional scheduling method (EPS®) was adopted to identify a schedule of mining activities that satisfied basic sets of constraints, including physical adjacencies of mining activities and operational resource capacity. Additional constraint scenarios explored were geotechnical and ventilation, which negatively effect development rates. Next, the automated SOT procedure was applied to determine whether the schedules could be improved upon. It was demonstrated that SOT permitted the rapid re-assessment of project value when new constraint scenarios were applied. This study showed that the automated schedule optimization added value to the project every time it was applied. In addition, the reoptimizing and re-evaluating was quickly achieved. Therefore, the tool used in this research produced more optimized schedules than those produced using conventional scheduling methods.Master of Applied Science (MASc) in Natural Resources Engineerin

A methodology for implementing the analytical hierarchy process to decision-making in mining

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfilment for the degree of Master of Science in Engineering Johannesburg 2015The Analytic Hierarchy Process (AHP) is a Multi Criteria Decision-Making (MCDM) tool, which has gained wide acceptance in all disciplines in science and engineering. Although it has been used in mining engineering applications, it is only recently gaining significant momentum in the mining industry. Given its simplicity, it may seem surprising that it has not received wide acceptance, but this is probably due to a lack of both publicity and a user-friendly methodology. This report introduces a simple methodology that can be employed by anyone who possesses basic knowledge of arithmetic and spreadsheets, without having to know or understand fully the mathematics that the process is based on.MT201

Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

A review of soft computing technology applications in several mining problems

Soft computing (SC) is a field of computer science that resembles the processes of the human brain. While conventional hard computing is run based on crisp values and binary numbers, SC uses soft values and fuzzy sets. In fact, SC technology is capable of address imprecision and uncertainty. The application of SC techniques in the mining industry is fairly extensive and covers a considerable number of applications.This paper provides a comprehensive overview of the published work on SC applications in different mining areas. A brief introduction to mining and the general field of SC applications are presented in the first section of the paper. The second section comprises four review chapters. Mining method selection, equipment selection problems and their applications in SC technologies are presented in chapters one and two. Chapter three discusses rock mechanics-related subjects and some of representative SC applications in this field. The last chapter presents rock blasting related SC applications that include blast design and hazards. The final section of the paper comments on the use of SC applications in several mining problemsand possible future applications of advanced SC technologies