A METHODOLOGY FOR TRUCK ALLOCATION PROBLEMS CONSIDERING DYNAMIC CIRCUMSTANCES IN OPEN PIT MINES, CASE STUDY OF THE SUNGUN COPPER MINE

Problem raspodjele kamiona smatra se jednim od najvažnijih čimbenika u postizanju planiranih proizvodnih kapaciteta u rudarstvu. Tradicionalne tehnike raspodjele kamiona (npr. matematičko programiranje, teorije čekanja u redu) podliježu različitim razinama pojednostavljenja u formuliranju stvarnoga prijevoza u heterogenim okolnostima. U ovome radu analiziran je problem raspodjele kamiona razvojem metode za optimizaciju raspodjele kamiona koja se temelji na simulaciji optimizacije (SBO) s obzirom na nesigurnosti tijekom rada kamionskoga voznog parka. Metoda osigurava integriranu strukturu simultanom kombinacijom optimizacije i simulacije stohastičkih diskretnih događaja. Ciljna je funkcija minimiziranje ukupnoga broja kamiona za transport sa simulacijom diskretnih događaja korištenih za modeliranje rubnih uvjeta. U ovome radu istražen je rad voznoga parka na primjeru rudnika bakra Sungun kako bi se postigla optimalna raspodjela kamiona pri različitim radnim operacijama na eksploatacijskome polju rudnika. Pojedinosti rada procijenjene su na temelju različitih pokazatelja kao što su iskorištenje, vrijeme čekanja i količina transportiranoga materijala za svaku radnu operaciju. K onačno, uska grla operacija prepoznata su za svaku situaciju.Truck allocation problems are considered as one of the most substantial factors in the achievement of planned production capacity in the mining industry. Traditional truck allocation techniques (e.g. mathematical programming, queueing theories) have undergone different levels of simplifications in formulating actual haulage operations under heterogeneous circumstances. In this study, the truck allocation problem is analysed through the development of the simulation-based optimization (SBO) method for the optimization of truck assignment considering uncertainties during fleet operation. This method provides an integrated structure by the simultaneous combination of optimization and stochastic discrete-event simulation. The objective function is to minimize the total number of trucks for haulage operation with discrete-event simulation employed to model the constraints. As a case study, the fleet operation of the Sungun copper mine is investigated to accomplish an optimal truck allocation for various working benches in the mine site. Operation details are evaluated through different indicators such as utilization, waiting times, and the amount of transported materials for each working bench. Finally, the operation bottlenecks are recognized for each situation

A simulation model for truck-shovel operation

A truck-shovel mining system is a flexible mining method commonly used in surface mines. Both simulation and queuing models are commonly used to model the truckshovel mining operation. One fundamental problem associated with these types of models is that most of the models handle the truck haulage system as macroscopic simulation models, which ignore the fact that a truck as an individual vehicle unit dynamically interacts not merely with other trucks in the system but also with other elements of the traffic network. Some important operational factors, such as the bunching effect and the influence of the traffic intersections, are either over simplified or ignored in such a macroscopic model. This thesis presents a developed discrete-event truck-shovel simulation model, referred to as TSJSim (Truck and Shovel JaamSim Simulator), based on a microscopic traffic and truck-allocation approach. The TSJSim simulation model may be used to evaluate the Key Performance Indicators (KPIs) of the truck-shovel mining system in an open pit mine. TSJSim considers a truck as an individual traffic vehicle unit that dynamically interacts with other trucks in the system as well as other elements of the traffic network. TSJSim accounts for the bunching of trucks on the haul routes, practical rules at intersections, multiple decision points along the haul routes as well as the influence of the truck allocation on the estimated queuing time. TSJSim also offers four truck-allocation modules: Fixed Truck Assignment (FTA), Minimising Shovel Production Requirement (MSPR), Minimising Truck Waiting Time (MTWT) and Minimising Truck Semi-cycle Time (MTSCT) including Genetic Algorithm (GA) and Frozen Dispatching Algorithm (FDA)

A Study on Application of Strategic Planning And Operations Research Techniques in Open Cast Mining

Mining happens to be the second oldest industry in the world considering the agriculture as the first and the foremost. That primitive society relied nearly on mined produce that is reflected aptly through nomenclature such as Stone Age, Copper Age, Bronze Age and Iron Age. These nomenclatures rightly capture the ethos of the time that shows increasing complexity of people’s society’s relationship with mining produce and use of metals. Our remote ancestors did practice mining on hard rock. Mining remained with their common occupation to earn livelihood and meet their needs. Since they had meager requirement of fuels; their major need of fuel was met mostly from dense forests on the earth. As the time passed, they required to meet ever increasing standards of living. As a result, demand of fuel was felt as extremely necessary for the existence of mankind and it kept on growing. In order to meet ever increasing demand, mining of coal took a shape in one way or the other.

Computer aided analysis and design of mine transportation systems

Haulage Costs account for a considerable portion of a surface mine's operational budget. It is therefore vital that, for a particular pit configuration, the optimum utilisation of the available truck fleet is adopted during the mine's life. Also, if the optimisation methods are established beforehand, it is possible to determine exactly how many trucks will be required. Both decisions can be made at the planning stage by the application of linear programming and discrete simulation to computer models of the haulage network. The project presented herein investigates the practicality of developing a general-purpose mine transportation selection and scheduling system within the context of a Computer Aided Design (CAD) environment. Compatibility with a purpose-built, interactive graphics package is shown to enable rapid, semiautomatic generation of model networks and the planning engineer is assisted further by the robust and friendly user-interface which has also been developed. Unlike a number of existing packages, which either make use of commercially available software on a stand-alone basis or were specifically designed for the analysis of a particular operation, this system is completely integrated with a central database which makes it applicable to any mine. The enhanced ability to produce valid mathematical solutions and their associated network models using the above systems, allows a large number of configurations and dispatching policies to be compared in a relatively short space of time. However, attention is also paid to the degree of correspondence with what can be achieved in reality since this will also effect the selection decision. All the modules mentioned form part of a much larger planning system currently being developed at The University of Nottingham, Department of Mining Engineering, known as NUmine

Contribution to the capacity determination of semi-mobile in-pit crushing and conveying systems

As ore grades decline, waste rock to ore ratios increase and mines become progressively deeper mining operations face challenges in more complex scenarios. Today´s predominant means of material transport in hard-rock surface mines are conventional mining trucks however despite rationalisation efforts material transportation cost increased significantly over the last decades and currently reach up to 60% of overall mining. Thus, considerations and efforts to reduce overall mining costs, promise highest success when focusing on the development of more economic material transport methods. Semi-mobile in-pit crusher and conveyor (SMIPCC) systems represent a viable, safer and less fossil fuel dependent alternative however its viability is still highly argued as inadequate methods for the long term projection of system capacity leads to high uncertainty and consequently higher risk. Therefore, the objective of this thesis is to develop a structured method for the determination of In-pit crusher and conveyor SMIPCC system that incorporates the random behaviour of system elements and their interaction. The method is based on a structured time usage model specific to SMIPCC system supported by a stochastic simulation. The developed method is used in a case study based on a hypothetical mine environment to analyse the system behaviour with regards to time usage model component, system capacity, and cost as a function of truck quantity and stockpile capacity. Furthermore, a comparison between a conventional truck & shovel system and SMIPCC system is provided. Results show that the capacity of a SMIPCC system reaches an optimum in terms of cost per tonne, which is 24% (22 cents per tonne) lower than a truck and shovel system. In addition, the developed method is found to be effective in providing a significantly higher level of information, which can be used in the mining industry to accurately project the economic viability of implementing a SMIPCC system

The application of probabilistic logic to identify, quantify and mitigate the uncertainty inherent to a large surface mining budget

Mining is a hugely expensive process and unlike manufacturing is based on an ever diminishing resource. It requires a continuous infusion of capital to sustain production. A myriad of factors, from the volatility of the markets to the surety that the minerals are really there, “plagues” both management and investors. The budget tries to predict or forecast future profits and acts as a roadmap to all stakeholders. Unfortunately, most of the time the budget of a mine degenerates to the extent of a collapse, sometimes very soon into the new budget period. This problem plagues both small and large mines indiscriminately. The budget is dictated in absolutes, and little or no variability is allowed. This thesis aims at developing a process to predict the probability of failure or success through the application of probabilistic logic to the simulation of the budget. To achieve this, a very detailed modelling tool is required. The model must replicate the actual mining process both in time and actual spatial representation. Enabling technology was developed over a period of five years, primarily based on the Runge Software Suite. The use of activity based costing enabled the budget to be simulated and expressed as a probability distribution. A Pareto analysis was done on the main cost drivers to extract the most important elements – or key drivers - that need to be manipulated. These distributions were mapped against real data and approximated with the use of the three parameter Weibull distribution. Simulation using Xeras® (Runge) proved to be impossible. This is due to the time needed for setup and processing. The budget was described as an empirical function of the production tonnages split according to the Pareto analyses. These functions were then utilised in Arena® to build a stochastic simulation model. The individual distributions are being modelled to supply the stochastic drivers for the budget distribution. Income, based on the sales, was added to the model in order for the Nett profit to be reflected as a distribution. This is analysed to determine the probability of meeting the budget. The underlying analysis of an open pit mining process clearly reflects that there are primary variables that may be controlled to trigger major changes in the production process. The most important parameter is the hauling cycle, because the haul trucks are the nexus of the production operation. It is further shown that the budget is primarily influenced by either FTE’s (full time employees, i.e. bodies) or funds (Capex or Opex) or a combination of both. The model uses probabilistic logic and ultimately culminates in the decision of how much money is needed and where it should be applied. This ensures that the probability of achieving the budget is increased in a rational and demonstrable way. The logical question that arises is: “Can something be done to utilise this knowledge and change behaviour of the operators?” This led to (IOPA – Intelligent Operator Performance Analyses) – where the performance or lack thereof is measured on a shift by shift basis. This is evaluated and communicated through automated feedback to the supervisors and operators and is being implemented. Early results and feedback are hugely positive. The last step is prove where capital (or any additional money spend) that is applied to the budget will give the most benefit or have the biggest positive influence on the achievement thereof. The strength of the model application lies therein that it combines stochastic simulation, probability theory, financial budgeting and practical mine schedule to predict (or describe) the event of budget achievement as a probability distribution. The main contribution is a new level of understanding financial risk and or constraints in the budget of a large (open pit) mine.Dissertation (MSc)--University of Pretoria, 2014.Mining EngineeringMEngUnrestricte

Truck Selection with the Fuzzy-WSM Method in Transportation Systems of Open Pit Mines

Open pit mines gain width and become more complicated as they are deeper today, and it is inevitable to carry the produced material with a truck transportation system. Therefore, in large-scale businesses, truck selection has great importance for the transportation costs to be sustainable. This study investigates the main factors and corresponding criteria influential in selection of trucks, which are the most frequent used means of transportation in open pit mines. Analytic hierarchy process and fuzzy weighted sum model are employed to solve the selection problem. Six different truck types and 20 selection criteria are considered. As a result of technical analysis, most suitable trucks are found

Belief Space Scheduling

This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches