The simulation as a stand alone optimization tool of a complex system such as a vertical integrated mining operation, significantly over simplifies the actual picture of the system processes involved resulting in an unaccountable effort and resources being spent on optimizing Non Value Added (NA) processes. This study purposed to develop a discrete stochastic simulation-optimization model to accurately capture the dynamics of the system and to provide a structured way to optimize the Value Added (VA) processes. The mine operation model to be simulated for this study is designed as a hybrid level throughput model to identify the VA processes in a mining operation. This study also allows a better understanding of the impact of variation on the likelihood of achieving any given overall result. The proposed discrete stochastic simulation- optimization model provides the ability for a process manager to gain realistic understanding of what a process can do if some factors constraining the process were to be optimized i.e. to conduct what-if analysis. Another benefit of this approached technique is to be able to estimate dependable and reasonable returns on a large optimization related expenditure. The inputs into the model are the capability of the processes which are entered using various variables depending on how much information is available; simple inputs for least amount of information to detailed inputs for well known process to combinational inputs for somewhere in between. The process bottlenecks are identified and measured using the outputs of the model which include production output, severity of constraints, capacity constraints and cumulative bottleneck plots. Once a base case has been identified and documented then the inputs can be modified to represent the business initiatives and the outputs can be compared to the base case to evaluate the true value of the initiative.

In Traveling Salesman Problem (TSP) with profits, a profit is associated with each city and the requirement to visit all cities is removed. The purpose is to simultaneously minimize cost (excluding as many cities as possible) and maximize profit (including as many cities as possible). Although the reduced single-objective case of the problem has been well-studied, the true biobjective problem has been studied only by a few researchers. In this paper we study the true biobjective problem using the Multiobjective Genetic Algorithm NSGA II and the Lin-Kernighan Heuristic. We propose several improvements for NSGA II in solving the problem. Based on these improvements, we provide computational results of the approximated Pareto-optimal front for a set of practically large size TSP instances. Finally, we provide a framework and its computational results for a post-optimality analysis to guide the decision maker, using the data mining software Clementine.

The aim of the thesis is to develop algorithm which will be able to optimize roles using RBAC model. The intent of the theoretical part is to analyze RBAC model and present current options which are available for role optimization. The practical part deals with development of algorithm which allows to optimize roles based on defined criteria from user. This algorithm is implemented in programming language Java and builds on Role Process Optimization Model (ROPM). In the last part is showed on example set of data how this algorithm works, step by step, with explanation of each step. Result of this algorithm is new RBAC model defined by user criteria. In this thesis are also listed different approach in role optimization, possible future development and concept of mapping RBAC model to mathematical and data-mining techniques.

Low-grade mineral deposits lead to a very high tonnage excavation with the adherent economical and environmental problems belong to gas emissions and minerals recovery costs, which, accompanied by the higher operational and equipment costs and the higher demand for the mineral resources, lead to increasing of mineral commodities prices, especially metals. These challenges can be overcome through mine planning optimization. Therefore, an approach for the global optimization of the integrated mining and processing operations is designed by a dynamic and simulation model construction. By applying a case study and through mining selectivity strategy, deeply investigation of the ore parameters (especially mineral liberation grain size and hardness), and proper arrangements for the plant facilities, mineral production is realized, with better quality, lower environmental impacts, lower costs, and higher economic benefits.:Table of Content List of Figures ………………………………………………………………………….……… V List of Tables …………………………………………………………………………….…… IX List of symbols and Abbreviations …………………………………………………............ XII List of Appendices …………………………………………………………..……............ XVIII 1. Justification and Importance of the Mine Planning Optimization ……………………….. 1 1.1 Introduction ............................................................................................................................... 1 1.2 Urgent need for general mine planning optimization ............................................................... 2 1.2.1 Overall costly low-grade ore deposits ................................................................................... 2 1.2.2 World markets ........................................................................................................................ 3 1.2.3 Sustainability requirements in mining, environmental and social issues .............................. 5 1.2.4 The strategic importance of the mining industry ................................................................... 6 2. State of the Science and General Outline for Mine Planning Optimization Concepts …... 8 2.1 The mine planning optimization concepts ................................................................................ 8 2.1.1 Improvements for the interconnected mining and processing operations ............................. 8 2.1.2 Urgent demand for the unit-operations cost reduction through holistic optimization ......... 12 2.1.3 Expenditures of size reduction operations ........................................................................... 13 2.1.4 The Mill as a critical point in the product supply chain ...................................................... 17 2.2 Critical review of researches for the (Mine-to-Mill) optimization field ................................. 18 2.2.1 Mill throughput optimization ............................................................................................... 18 2.2.2 Intelligent assistant systems and processes automation and monitoring …………………. 19 2.2.3 Scheduling software and operationally holistic modules ……………………………...…. 20 2.3 The aim of work and the thesis layout .................................................................................... 22 3. Suggested Approach for a Holistic Mine-to-Mill Optimization ……………………….… 25 3.1 Introduction and scope …………………………………………………………………….. 25 3.2 The methodology plan …………..………………………………………………………….. 26 3.3 Assignment of the operational parameters inter-acting the integrated optimization ……….. 29 3.3.1 Mining and processing activities …………………………………………………………. 29 3.3.2 Mining and processing operational parameters …………………………………………... 31 3.3.3 Mining and processing special indicators ………………………………………………… 42 3.4 Introduction to the dynamic modeling and simulation softwares ………………………...… 45 3.5 Particular concepts belonging to the chosen modeling software ………………………...…. 46 3.6 Main tools, components and constituents of the used software …………………………..… 49 3.7 Assumed case study for the model construction ……………………………………….…… 51 4. Calculation Basics for Applying Dynamic Modeling and Simulation for the Mining and Processing Operations ……………………………………………………………………….... 53 4.1 The modeling construction strategy ………………………………………………………… 53 4.2 Construction of the [Reference-Mode] model …………………………………………….... 54 4.2.1 Dynamic modeling and simulation for the drilling and blasting operation ………………. 54 4.2.2 Dynamic modeling and simulation for the loading and hauling operations …………..….. 62 4.2.3 Dynamic modeling and simulation for the crushing and grinding operations …………..... 71 5. Case Study Application and the Model Output and Assessment ……………………...… 82 5.1 Main physical properties of the ore deposit under study ………………………………..….. 82 5.2 Principal technological and operational parameters within the case study ……………....… 83 5.3 Processing of the data from the case study ………………………………………………… 86 5.4 [Reference-Mode] model results and assessment ………………………………………...… 87 5.4.1 Preliminary main results of the mining activities sub-models ………………………...….. 87 5.4.2 Preliminary main results of the processing activities sub-model ……………………..….. 97 5.4.3 Further model optimization requirements ……………………………………………….. 105 6. The Model Optimization, Validation and Practical Applications ………………..…….. 107 6.1 Model further optimization plan …………………………………………………….…….. 107 6.2 The ore deposit characteristics and details …………………………………………….….. 108 6.2.1 Tonnage distribution and cut-off-grade for the ore deposit ……………………………... 108 6.2.2 Liberation size and microscopic grain size distribution for the ore deposit …………….. 112 6.3 Mining selectivity and processing mixing scenarios …………………………………….... 113 6.3.1 Blending triangle design for choice of the annual mining contribution scenarios ……… 113 6.3.2 Planed processing strategies according to the pre- and post-grinding mixing ………..… 115 6.4 An Excel calculation tool for preparing the new detailed inputs to the modified model .… 118 6.4.1 The need for new prepared and detailed inputs to the modified model ……………….… 118 6.4.2 Description and benefits of the designed Excel calculation tool ……………………..…. 118 6.4.3 The main outputs of the Excel calculation tool ……………………………………….… 120 6.4.4 The Excel calculation tool outputs as inputs to the modified Vensim model ………….... 120 6.5 The model modification through the new added mathematical and functions ……………. 123 6.6 [Controlled] model results and the comparable discussion of the processing strategies ..… 129 6.6.1 General notifications for the model handling and the results presentation …………….... 129 6.6.2 Results of the mining section of the model …………………………………………….... 130 6.6.3 Results of the processing section of the model ……………………………………….…. 132 6.6.4 Comparison between the three data processing and arrangement methodologies ……..... 142 6.6.5 Comparison between scenarios ………………………………………………………….. 149 6.6.6 Extreme cases versus the chosen Organized Method ………………………………….... 153 6.7 Optimization evolution overview across the operations improvement steps …………...… 157 7. Conclusion and Recommendations …………………………………………………...… 163 References …………………………………………………………………………………… 168 Appendices ……………………………………………………………………………...…… 179