Applications of simulation and optimization techniques in optimizing room and pillar mining systems

The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes --Abstract, page iii

Stochastic-optimization of equipment productivity in multi-seam formations

Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii

Open Pit Production Scheduling applying Meta Heuristic approach

Production scheduling of a mine is required for effective and economic operations of a mine. Here we are trying to perform production scheduling of mining of mineral blocks under some specific constraints to maximize the profit. The large number of variables and inequalities involved in the process makes it nearly impossible to solve using classical optimization techniques. The techniques and softwares available take a huge amount of time to produce optimized solutions. In this project Genetic Algorithm, a metaheuristic algorithm, has been considered to perform the optimization. The solution provided may not be optimized but will be very nearly optimized and will take significantly lesser time. It starts from a random solution performing several crossovers, mutations and eliminations to reach the optimized solution. A study was carried out in an open pit iron ore mine. The NPV of the mine was found to be a cumulative of over 551 million $. The average stripping ratio was calculated to be 1.72 over the period of 4 years. The computational time required to solve the problem was 31 mins

遺伝的アルゴリズムの改良に基づくマルチターゲットの運輸問題に関する研究

With the rapid development of economic globalization and information technology, rapid changes have taken place in all fields of society. The status of modern logistics industry in the process of the flow of social means of production and commodities has become increasingly prominent, accompanied by profound changes in production and manufacturing, material circulation, commodity transactions and management methods. Logistics cost accounts for a large share of national GDP, which can reflect the quality and scale of a country\u27s national economy, reduce the logistics cost of enterprises, and greatly improve the profit space. Especially under the background of economic globalization, the competition among enterprises is increasingly fierce, and the impact of logistics on the competitiveness of enterprises is increasingly obvious. In the modern e-commerce environment, with the rapid development of science and technology, the space for enterprises to obtain profits from the products themselves has been greatly reduced. In order to reduce costs and improve profits as much as possible, enterprises focus on logistics. In the whole logistics system, transportation is a very important link. Therefore, efforts to reduce the cost of logistics and transportation can greatly reduce the cost of the entire logistics system. This paper starts from the main factors involved in the transportation logistics, optimizes the main factors affecting the logistics, reduces costs and improves profits.Firstly, this paper discusses and studies the distribution personnel, mainly including the logistics distribution under the limitation of personnel fatigue and the delivery distribution mode under the new mode of personnel allocation - "crowdsourcing logistics". Aiming at the research on the limitation of fatigue, aiming at the maximization of customer satisfaction and the minimization of total cost, this paper constructs a model of path optimization for driver\u27s fatigue driving, and designs a single Partheno-genetic algorithm for the model, which is verified by the distribution case of Japan\u27s otaku. On the research of crowdsourcing delivery, taking the delivery network as the research object, this paper analyzes the distribution process, mode and existing problems of crowdsourcing delivery mode. Based on the purpose of optimizing the distribution network, taking the shortest distribution path and the least time delay as the objective function, the basic optimization model and dynamic optimization model of crowdsourcing distribution path with time window are established, and the rationality of the model is evaluated.Secondly, from the perspective of vehicle research and analysis, mainly study the two-tier node logistics distribution mode based on heterogeneous vehicles. This paper analyzes the common transportation vehicle selection problem in the existing transportation. Based on the genetic algorithm, taking the transportation cost of the double-layer logistics node of a city\u27s seafood products as the optimization goal, and comprehensively considering the problem of taking delivery vehicle route and vehicle configuration strategy of different routes at the same time, the mathematical model of vehicle scheduling and transportation route problem in the double-layer node transportation route is established. In this paper, MATLAB software is used to solve the model based on traditional genetic algorithm and Partheno-genetic algorithm, and the correctness and effectiveness of the model and Partheno-genetic algorithm are verified.Then, from the perspective of transportation path mode, the research mainly involves the current hot "multimodal transport" problem. In this paper, the coal transportation in a country is taken as the research object. Under the mode of "iron water combined transportation", how to reasonably distribute the transportation capacity and correctly select the transportation mode can realize the enterprise to control the logistics cost and ensure the maximum profit. At the same time, based on the traditional genetic algorithm mechanism, aiming at the premature and local search ability of the traditional genetic algorithm in solving the logistics transportation path optimization problem are analyzed Due to the shortage of power, a hybrid genetic algorithm is proposed to solve the model.Finally, the optimization algorithm of logistics distribution is discussed. This paper presents a hybrid genetic algorithm based on information entropy and game theory. First, the initial population is generated by calculating population diversity with information entropy. Combined with parallel genetic algorithm, standard genetic algorithm (SGA), Partheno-genetic algorithm (PGA) and hybrid genetic algorithm (sga-pga) which integrates standard genetic algorithm and Partheno-genetic algorithm (sga-pga) are used to perform evolutionary operations. At the parallel node, information entropy and fitness value of each sub population are used Finally, three programs checking functions Rosenbrock function, Rastrigin function and Schaffer function are introduced to analyze the performance superiority of the algorithm.博士(工学)法政大学 (Hosei University

Water truck routing optimization in open pit mines using the general algebraic modelling system approach

This paper presents a methodological approach for routing optimization in open pit mines which is a trending topic for dust emission reduction in mining process. In this context, the aim of the research and its contribution to the knowledge is firstly described based on a comprehensive literature survey in the field. Then, as an arc routing problem, the mathematical model for the process is generated including the objective function, minimizing the total distance traveled by the water truck fleets, practical constraints that should be met and the used assumptions. Finally, the formulated optimization problem solved employing General Algebraic Modelling System (GAMS) approach respect to the nature of the mathematical equations. The tested results by simulations discussed to confirm the effectiveness of the proposed method in dealing with the in-hand problem. This methodological approach could be used in optimization of other similar engineering problem as well

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility

The global mineral industry faces constant challenges that are incited and intensified by market uncertainty. Demand constrictions, resource scarcity, and market volatility all generate market risk that is compounded by the high capital and long payback periods inherent to mining projects. Quantitative risk assessments provide a methodology to leverage uncertain economic scenarios and accurately assess profitability; however, current mine valuation techniques and engineering economic approaches tend to scrutinize the uncertainty of technical factors, such as ore grade and metallurgical recovery, to a much greater degree than market factors, like price-demand restrictions. Nevertheless, the optimal operating conditions for mining, mineral processing and refining must reflect the true dynamics of uncertain commodity prices, and typical operational responses, such as modifications to mine production and material stockpiling.;This thesis presents a new mineral asset valuation methodology based on economic uncertainty in the commodity market and operational flexibility for mining operations. This novel valuation approach resulted in the generation of a complex system that consists of three primary components. First, a price forecasting component was used to generate future commodity price scenarios with two different stochastic differential equation models (Geometric Brownian Motion and Mean-Reverting-drift). Second, a dynamic methodology of discounted cash flow (DCF) was developed, allowing operational flexibility for mining, processing, stockpiling, and selling material. Third, two distinct optimization techniques (Interior-point method and genetic algorithms) were applied for identification of optimal operating parameters in a mining operation, with a particular focus on using buffer stockpiles to ameliorate the impacts of volatile price fluctuations. The dynamic model was applied in a case study assessing the valuation of a greenfield Ni-Co-Sc mine project. The hypothetical deposit was subjected to different levels of commodity price trends, price volatility, discount rates and maximum stockpiling capacity. Overall, the dynamic valuation model obtained NPV results ranging from 2% to 11% higher than standard static DCF techniques. Operational flexibility and ore inventory management proved to be crucial for profit increase on the project

Optimised decision-making under grade uncertainty in surface mining

Mining schedule optimisation often ignores geological and economic risks in favour of simplistic deterministic methods. In this thesis a scenario optimisation approach is developed which uses MILP optimisation results from multiple conditional simulations of geological data to derive a unique solution. The research also generated an interpretive framework which incorporates the use of the Coefficient of Variation allowing the assessment of various optimisation results in order to find the solution with the most attractive risk-return ratio

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners