NPV risk simulation of an open pit gold mine project under the O'Hara cost model by using Gas

This paper analyzes an open pit gold mine project based on the O'Hara cost model. Hypothetical data is proposed based on different authors that have studied open pit gold projects, and variations are proposed according to the probability distributions associated to key variables affecting the NPV, like production level, ore grade, price of ore, and others, so as to see what if, in a gold open pit mine project of 3000 metric tons per day of ore. Two case scenarios were analyzed to simulate the NPV, one where there is low certainty data available, and the other where the information available is of high certainty. Results based on genetic algorithm metaheuristic simulations, which combine basically Montecarlo simulations provided by the Palisade Risk software, the O'Hara cost model, net smelter return and financial analysis tools offered by Excel are reported, in order to determine to which variables of the project is more sensitive the NPV.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Optimizing reliability, maintainability and testability parameters of equipment based on GSPN

Reliability, maintainability and testability （RMT） are important properties of equipment, since they have important influ- ence on operational availability and life cycle costs （LCC）. There- fore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of all, the model for the equipment operation pro- cess is established based on the generalized stochastic Petri nets （GSPN） theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Finally, the effectiveness and practicability of this approach are validated through an example.

Landscape Pattern Optimization of Chengdu Based on Modern Garden City

TOptimization of regional landscape pattern is significant for improving function and value of ecosystem,and restraining the expansion of urban layout.Taking Chengdu City for example,this paper applied RS and GIS techniques,landscape indexes and ecological service function evaluation to further analyze the temporal and spatial characteristics of landscape pattern and spatial differences of regional ecological functions,and on this basis,identified the spatial distribution of ecological source lands.Based on the long-term objective of building Chengdu into a modem garden city,this paper applied the accumulative cost distance model and introduced garden city theory to construct regional ecological corridors and ecological nodes,and explored the approaches of optimizing landscape pattern of modem garden city.The results showed that a great deal of arable land has been transferred to construction land in the urbanization;intensity of regional ecological functions showed obvious spatial differences;ecological source lands were mainly distributed in the Longmen Mountain,the Qionglai Mountain,the Changqiu Mountain and the Longquan Mountain;according to actual conditions of the study area,the road ecological corridors,river corridors and agricultural corridors in the layout of "four rings and six radial corridors" were constructed;ecological nodes dominated by intersection,wetland and forest park were formed.This research method and results are significant references for building Chengdu into a modem garden

Optimal dividend and capital injection problem with a random time horizon and a ruin penalty in the dual model

In the dual risk model, we consider the optimal dividend and capital injection problem, which involves a random time horizon and a ruin penalty. Both fixed and proportional costs from the transactions of capital injection are considered. The objective is to maximize the total value of the expected discounted dividends, and the penalized discounted both capital injections and ruin penalty during the horizon, which is described by the minimum of the time of ruin and an exponential random variable. The explicit solutions for optimal strategy and value function are obtained, when the income jumps follow a hyper-exponential distribution.Besides, some numerical examples are presented to illustrate our results.

Techno-economic analysis of municipal wastewater land treatment systems in China

This paper analyses the capital costs, power consumption and operation costs of municipal wastewater land treatment systems, including rapid infiltration, slow-rate infiltration, overflow and constructed wetland, by means of series engineering design. The results show that land treatment can save 50-70% of capital costs, 80-90% of power and 75-85% of operation costs when compared with secondary treatment.

Dynamics Models of Crank in Motor Housing Process

With regards to the assembly line of cost control of Dechang(HK)company,the motor housing’s cost control of process will be necessarily respected.Because the supply quantity is big in a machine the price of motor housing is small,so that the cost control of automatic production line is significant with modeling.It is found that the control of equipment includes in shaft and crank linkage for benefit which also needs to be controlled in detail.For the sake of benefits can we fundamentally resolve the main problem of high cost process.

Engineering costs proxy models for China rural access network and its application

The article builds three engineering rural access network models that describe the structure of network elements and their relative engineering parameters for cable access, synchronous code division multiple access（SCDMA）, and very small aperture terminal （VSAT） access technologies in the rural areas of China. Of the three access technologies, cable access and SCDMA access are the most popular access technologies. Besides, there still exist some remote special areas such as western mountain areas, whose natural environment is so bad that VSAT becomes the unique economical access way. Fully considering rural areas＇ geographical environments＇ impact, the article introduces geographical revised factor （GRF） to the models. By substituting the network data from the operators into the models, the article obtains the integrated networking values and does further researches on different access networks.

Cost analysis model for catalytic conversion of syngas in to light hydrocarbon gases

Bio-gasification is a new technology and considered as a more efficient way to utilize bioenergy.The economic feasibility becomes one of the greatest issues when we apply this new technology.Evaluation of economic feasibility of a bio-gasification facility needs better understanding of its production unit cost under different capacities and different working shift modes.The objective of this study was to evaluate the unit cost of biofuel products(Liquid HCs,Light HCs and Oxygenates CxHyOz)under different capacities using a modeling method.The cost analysis model was developed using Visual Basic Microsoft 2008,computer programming language and mathematical equations.The modeling results showed that the unit costs of biofuel product from bio-gasification facility were significantly affected by production capacities of facilities.As the facility capacity increased from 65 to 10,000 N m^3 h^-1,the biofuel production unit cost of gas(Light HCs),oil(Liquid HCs),and aqueous(Oxygenates CxHyOz)decreased from$38.92 per MMBTU,$30.89 per gallon and$25.74 per gallon to$2.01 per MMBTU,$1.59 per gallon,and$1.33 per gallon,respectively.The results of the sensitivity analysis showed that feedstock cost was the most sensitive cost factor on unit costs for all biofuel products at high capacity.The cost analysis model developed in this study could be used to optimize production unit costs of bio-fuel products from bio-gasification facility.

A LIMITED ORDER CAPACITY STOCHASTIC INVENTORY MODEL WITH A FIXED COST FOR ORDER: THE DISCOUNTED CASE

This paper considers a single-item, periodic-review inventory model with linear ordercosts, a convex function representing expected one-period costs, nonegative i.i.d. demandsand a fixed cost for order. Stockouts are backordered. All data are stationary Both finiteand infinite horizon problems are treated.

A Confidence-based Consensus Model for Multi-Attribute Group Decision Making:Exploring the Bounded Trust Propagation and Personalized Adjustment Willingness

Social trust network(STN)and minimum cost consensus(MCC)models have been widely used to address consensus issues in multi-attribute group decision-making(MAGDM)problems with limited resources.However,most researchers have overlooked the decision maker‘(DMs)’confidence levels(CLs)and adjustment willingness implicit in their evaluations.To address these problems,this paper explores a confidence-based MCC model that considers DMs’adjustment willingness in the STN.The proposed model includes several modifications to the traditional trust propagation and consensus optimization models.Firstly,the improved method for measuring CLs of DMs and the confidence-based normalization approach are defined,respectively.Secondly,the bounded trust propagation operator is proposed,which considers the credibility of mediators to complete the STN.Thirdly,the identification rules based on the consensus index and CL are defined,and the MCC model with personalized cost functions and acceptable adjustment thresholds is built to automatically generate adjustment values for non-consensus DMs.Finally,a model to identify the non-cooperative behavior at the element level is established and the hybrid MCC model with persuasion strategies is provided.Finally,a case study is processed to verify the applicability of the proposed model,and comparison and sensitivity analysis are conducted to highlight its benefits.

HC-Store： putting MapReduce＇s foot in two camps

MapReduce is a popular framework for large- scale data analysis. As data access is critical for MapReduce＇s performance, some recent work has applied different storage models, such as column-store or PAX-store, to MapReduce platforms. However, the data access patterns of different queries are very different. No storage model is able to achieve the optimal performance alone. In this paper, we study how MapReduce can benefit from the presence of two different column-store models - pure column-store and PAX-store. We propose a hybrid storage system called hybrid columnstore （HC-store）. Based on the characteristics of the incoming MapReduce tasks, our storage model can determine whether to access the underlying pure column-store or PAX-store. We studied the properties of the different storage models and create a cost model to decide the data access strategy at runtime. We have implemented HC-store on top of Hadoop. Our experimental results show that HC-store is able to outperform PAX-store and column-store, especially when confronted with diverse workload.

Analysis of value of flexibility in Japan’s power system with increased VRE

The growth of renewable energy has accelerated globally toward a low-carbon economy since the Paris Agreement entered into force in 2016.As a result of the increase of variable renewable energy(VRE),namely solar PV and wind,power systems require more flexibility from conventional power plants with less power generation to regulate increased variability.There are sources of flexibility other than conventional power plants,including enhanced power networks,storage capacity and demand response.To maximize economic utilization of VRE power generation,it is necessary to use the flexibility potential from all these sources.In Japan,the share of VRE has increased since the introduction of a feed-in tariff(FIT)and,in parallel,power market reform is underway.Japan has a unique power system of nine grids connected like a fish bone,making the uptake of an increasing share of VRE challenging.This paper assesses the value of flexibility by source in Japan’s power system in 2030.An analysis of different VRE scenarios is undertaken based on a newly developed production cost model.The result of the simulation shows the quantitative impact of each source of flexibility to the generation cost and VRE curtailment and demonstrates the mechanism by which flexibility works to impact VRE curtailment.

Quantitative Analysis of Multi-Recovery-Based Intrusion Tolerance Model

Quantitative analysis has always been a difficult problem in security analysis of intrusion tolerance systems. An intrusion tolerance model based on multiple recovery mechanisms is introduced in this paper and how to quantify the security attributes of the model is proposed. A state transition model with recovery states more accurately describes the dynamic behavior of the system. Considering that recovery mechanisms have a great impact on the security performance of the system, we set up the cost models corresponding to different recovery mechanisms. We propose a feasible security measure based on mean cost to security failure in order to evaluate the system cost during the recovery phase. The experimental results confirmed the feasibility of the proposed methods.