Cost Estimation Model Research Articles

Study on highway transportation greenhouse effect external cost estimation in China

This paper focuses on estimating highway transportation greenhouse gas emission volume and greenhouse gas external cost in China. At first, composition and characteristics of greenhouse gases were analysed about highway transportation emissions. Secondly, an improved model of emission volume was presented on basis of highway transportation energy consumption, which may be calculated by virtue of main affecting factors such as the annual average operation miles of each type of the motor vehicles and the unit consumption level. the model of emission volume was constructed which considered not only the availability of energy consumption statistics of highway transportation but also the greenhouse gas emission factors of various fuel types issued by IPCC. Finally, the external cost estimation model was established about highway transportation greenhouse gas emission which combined emission volume with the unit external cost of CO2 emissions. An example was executed to confirm presented model which ranged from 2011 to 2015 Year in China. The calculated result shows that the highway transportation total emission volume and greenhouse gas external cost are growing up, but the unit turnover external cost is steadily declining. On the whole overall, the situation is still grim about highway transportation greenhouse gas emission, and the green transportation strategy should be put into effect as soon as possible.

A new model for estimation of project total cost in construction projects

This paper presents a new framework for the cost estimation of construction projects concerning the significant issues that contractors may face through the life cycle of the projects. The proposed approach is basically constructed on the basis of cost estimation process in the earned value management (EVM) technique. However, it attempts to resolve the present shortcomings in EVM estimation process and take into the consideration the cost-related issues so-called financial issues such as delay in client payment, and the time value of money. Furthermore, in order to cope with the uncertain conditions of real situations, the presented model takes the advantage of fuzzy sets theory which is a well-known method in dealing with the situations where the uncertainty arises. The proposed approach not only extends the theoretical framework of EVM but also gives a real insight into the project future performance. Finally, ten illustrative cases related to construction projects are provided to compare the obtained results of proposed model with the results of the EVM estimation process and to demonstrate how the model can be implemented in real projects.

A new model for estimation of project total cost in construction projects

A new model for estimation of project total cost in construction projects

Software cost estimation: Process, Methods and Tools

Software Cost Estimation is widely considered to be a weak link in software project management. It requires a significant amount of effort to perform it correctly. Errors in Software Cost Estimation can be attributed to a variety of factors. Various studies in the last decade indicated that 3 out of 4 Software projects are not finished on time or within budget or both. The problem lies in the estimation of the amount of effort required for the development of a project. Several different techniques for performing software cost estimation, including expert judgment and algorithmic models are available. Expert judgment is a common way of estimating the effort required for a project. Unfortunately, this method of estimation does not emphasize re-estimation during the project life cycle, which is an important part of project tracking, because it allows the estimates to be improved during the project life cycle. COCOMO is a popular algorithmic model for cost estimation whose cost factors can be tailored to the individual development environment, which is important for the accuracy of the cost estimates.

Cyclomatic Complexity for Determining Product Complexity Level in COCOMO II

Cost Construction Model (COCOMO) is an algorithmic software development cost estimation model, which requires accurate input values for each attribute. Input misjudgment on an attribute will have a great impact on the estimated effort. One of the attributes in COCOMO II is product complexity. Currently, the level of product complexity is assessed subjectively by an expert. This approach tends to be inaccurate because it is influenced by emotions, opinions and experiences. This paper proposes Cyclometer, a new approach based on the cyclomatic complexity metric to measure the product complexity level objectively. A comparison study was carried out and showed that the results comply with the expert judgments as indicated by moderate kappa statistic values.

Comparative Analysis of Common Software Cost Estimation Modelling Techniques

As software cost estimation in software projects is a very difficult, confusing and challenging work for any software company and software cost estimation is the primary step to start any software project. It gives the overview of resources, efforts and time/schedule required for a software project in respect of cost to the software company. Software project success generally depends on software cost estimation as it provides us an initial idea of the track, challenges and risk involved in the software project development. The software cost estimation in software engineering is very challenging to match the actual cost of the software project with estimated cost. Effective software cost estimation can help software company make more consistent decisions in planning the software project risk. If the predicted estimates are wrong it may lead to negative results for a software company. Many software companies find, search and analyze software project performance by estimating software cost estimation accuracy. Unfortunately, regardless of the large body of experienced and skilled with estimation models, the accuracy of these models is not adequate. In this research paper observation on the performance of the software cost estimation methods and description of methodologies and technique used in to software project cost estimation included. This research paper give comparative comparison study of software cost estimation methods and reviews several classes of software cost estimation models and techniques. Also study the pros and cons of different software cost estimation modeling techniques.

Cost Analysis and Development Strategies for China’ Natural Gas Power Generation Industry Under the Situation of Energy Price's Reformation

Abstract Driven by Chinese government, China's natural gas power generation industry has to deal with the energy price's reformation, the price of natural gas, electric power and some other energy source will be made by market instead of government. In recent years, China's natural gas power generation industry has made an unprecedented progress in the development of improving the energy structure and promoting Green Economy greatly. By establishing the cost estimation model of natural gas power generation and using the method of analysis for sensitivity factor, we think that the prime cost of natural gas is high in aspect of energy source cost. However, from the point of view of carbon emission cost, the cost of natural gas power generation is lower than coal power generation. On the other side such as power generation equipment, most of constant costs has been spent on imported power generation equipment. Therefore, Chinese natural gas power generation industries should work together with the China government for reducing the price of natural gas, realizing the gas turbine home-made, establishing the natural gas storage system and finally stabilizing natural gas supply.

A Comparison between Various Software Cost Estimation Models

Effective cost estimation is one of the most important and complex tasks in development of any software project. The stakeholders require a simple and effective model to enable them to make an efficient estimate of the software development cost. Estimation, basically, means to predict the value and predictions tend to go wrong. The whole process requires a huge amount of data, which is often challenging to collect, hence, making the whole process challenging. In this paper, we try to illustrate various existing cost estimation models proposed over the years and finally present a comparison between them. The paper also tries to pinpoint the various reasons for errors in cost estimation. Keyword: COCOMO, SLOC, Function Points, Fuzzy, Neural Net.

도시철도 투자타당성 평가를 위한 운영비용 추정모형 개발

도시철도 투자타당성 평가를 위한 운영비용 추정모형 개발

Cost Estimation Model for Polyacrylonitrile-Based Carbon Fiber Manufacturing Process

A polyacrylonitrile (PAN)-based carbon fiber (CF) manufacturing cost estimation model driven by mass is presented in this study. One of the biggest limiting factors in the large-scale use of carbon fiber (CF) in manufacturing is its high cost. The costs involved in manufacturing the carbon fiber have been formalized into a cost model in order to facilitate the understanding of these factors. This can play a key role in manufacturing CF in a cost-effective method. This cost model accounts for the fixed and variable costs involved in all the stages of manufacturing, in addition to accounting for price elasticity.

Additive manufacturing cost estimation for buy scenarios

Purpose To design for additive manufacturing (AM), the decision to use AM needs to be taken early in the product development process. Therefore, engineers need to be able to estimate AM part cost based on the few parameters available at this point in the process. This paper aims to develop suitable cost estimation models for this purpose, focusing on buy scenarios, as many companies choose to buy parts at service providers. Design/methodology/approach This study applies analogical cost estimation techniques to a data set of price quotations for laser sintering and laser melting parts. Findings The paper proposes easy-to-apply cost estimation models for laser sintering and laser melting for buy scenarios. Further, it generates new insights on the AM service provider market. Research limitations/implications The proposed models are only suitable for buy scenarios and are only a snapshot of cost achievable in 2014. Practical implications The proposed cost estimation models enable engineers to approximate AM part costs early in the product development process and thereby ease the decision to rapid manufacture certain parts. Originality/value This study addresses two gaps in the AM cost literature. It is the first study to take a qualitative approach to AM cost estimation, which is more suitable early in the product development process than the currently available quantitative studies. Further, it develops the first cost estimation for buy scenarios.

Shipping efficiency comparison between Northern Sea Route and the conventional Asia-Europe shipping route via Suez Canal

The continuous retreat of Arctic sea ice and seemingly appealing cost competitiveness of transarctic shipping routes are expected to boost shipping activities in the region. However, in reality, the number of Arctic transits remains meagre compared with major shipping routes. This study first develops a profit estimation model for containership sailing from an original port to a destination port with multiple port calls and a cost estimation model for oil tanker sailing from an origin port to a destination port. The authors then proceed to compare the shipping efficiency between the Northern Sea Route (NSR) and the Asia-Europe shipping route via Suez Canal by using the developed models and real shipping operational data. The results demonstrate that NSR shipping is not economically favored compared to traditional one in container shipping, but may be only appealing to small or medium-size tanker operators.

Energy Production from Biogas: Competitiveness and Support Instruments in Latvia

Abstract Use of renewable energy sources (RES) might be one of the key factors for the triple win-win: improving energy supply security, promoting local economic development, and reducing greenhouse gas emissions. The authors ex-post evaluate the impact of two main support instruments applied in 2010-2014 – the investment support (IS) and the feed-in tariff (FIT) – on the economic viability of small scale (up to 2MWel) biogas unit. The results indicate that the electricity production cost in biogas utility roughly corresponds to the historical FIT regarding electricity production using RES. However, if in addition to the FIT the IS is provided, the analysis shows that the practice of combining both the above-mentioned instruments is not optimal because too high total support (overcompensation) is provided for a biogas utility developer. In a long-term perspective, the latter gives wrong signals for investments in new technologies and also creates unequal competition in the RES electricity market. To provide optimal biogas utilisation, it is necessary to consider several options. Both on-site production of electricity and upgrading to biomethane for use in a low pressure gas distribution network are simulated by the cost estimation model. The authors’ estimates show that upgrading for use in a gas distribution network should be particularly considered taking into account the already existing infrastructure and technologies. This option requires lower support compared to support for electricity production in small-scale biogas utilities.

An extended cost estimation model for a multi‐material topology optimization approach

AbstractThe manufacturing costs of hybrid parts comprise several different proportions depending on the manufacturing process and the materials used. A simplified cost model based on the material weights is therefore no longer sufficient.In this paper, an approach for the weight and costs optimization of hybrid parts is presented. An extended costs calculation function regarding additional manufacturing costs and the integration of a new cost objective in the optimization process is investigated. The effects of the consideration of costs on the optimization results will be qualitatively shown on a simple example structure. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Software Development Cost Estimation: A Survey

Objectives: The present study is undertaken to survey the Software development cost estimation techniques. This study will provide guidelines and for researchers and practitioners of software engineering. Methods/Analysis: The study was undertaken by planning, conducting and reporting the literature review (LR) for the years 1991-2016. Findings: The study revealed that several SDCE models have been introduced. The reason for the evolution of software cost estimation models may be the changing nature of software complexity, i.e., one cannot exactly predict the cost for the whole project. Not only conventional empirical and quantitative methods but several data mining and machine learning techniques are also used for improved results. However, it is revealed that from quantitative to empirical all SDCE models can be used alone or hybrid with robust ML or DM techniques to estimate the software development exertion.

Carbon reduction cost estimating of Chinese coal-fired power generation units: A perspective from national energy consumption standard

The coal-fired power sector is the main source of CO2 emissions in China. This paper proposes a carbon abatement cost estimate model based on computing the emissions gap between the Chinese national energy consumption standards of units (NECS) and the units' coal consumption for per unit electricity supply (CCES). By applying the proposed model, the abatement costs of four coal-fired power generation units (600 MW USC, 1000 MW USC, 265 MW IGCC, 600 MW SC CFB) with or without Carbon capture and storage (CCS) are estimated in six combined scenarios. The results show that (1) the units' total cost would increase more than one time after conducting CCS while the unit cost of carbon emissions abatement would decrease more than 64%; (2) the 1000 MW USC unit with CCS has the largest carbon reduction potential and the lowest unit abatement cost among all units in all scenarios; (3) 265 MW IGCC unit would have no carbon reduction potential after 25 service years, and its service years may be shortened; and (4) the 256 MW IGCC and 600 MW SC CFB is the most sensitive to the average annual decline rate of NECS but the four units of installed CCS are not.

Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China

Cost estimation is one of the most critical tasks for building construction project management. The existing building construction cost estimation methods of many countries, including China, require information from several sources, including material, labor, and equipment, and tend to be manual, time-consuming, and error-prone. To solve these problems, a building construction cost estimation model based on ontology representation and reasoning is established, which includes three major components, i.e., concept model ontology, work item ontology, and construction condition ontology. Using this model, the cost estimation information is modeled into OWL axioms and SWRL rules that leverage the semantically rich ontology representation to reason about cost estimation. Based on OWL axioms and SWRL rules, the cost estimation information can be translated into a set of concept models, work items, and construction conditions associated with the specific construction conditions. The proposed method is demonstrated in Protégé 3.4.8 through case studies based on the Measurement Specifications of Building Construction and Decoration Engineering taken from GB 50500-2013 (the Chinese national mandatory specifications). Finally, this research discusses the limitations of the proposed method and future research directions. The proposed method can help a building construction cost estimator extract information more easily and quickly.

Innovations on Bayesian Approaches of Software Cost Estimation Model

Objectives: To find Large Software Products Cost Estimation Model by using Bayesian Approaches. Methods/Statistical Analysis: Composite strategy for building programming models in view of a blend of information and master judgment is tried here. This system depends on the surely knew and generally acknowledged Bayes’ hypothesis that has been effectively connected in other building areas incorporating to some degree in the product unwavering quality designing space. Be that as it may, the Bayesian methodology has not been viably misused for building more powerful programming estimation models that utilization a change adjusted blend of undertaking information and master judgment. The center of this paper is to demonstrate the change in precision of the cost estimation model when the Bayesian methodology is utilized versus the numerous relapse approach. Findings: We employed Bayesian model aligned utilizing a dataset of 100 datapoints approved on a dataset of 200 datapoints (sample data), it yields an expectation exactness of PRED(.30) = 76% (i.e., 106 or 76% of the 200 datapoints are evaluated inside 29.5% of the actuals). The immaculate relapse based model aligned utilizing 100 datapoints when accepted on the same 200 task dataset yields a poorer precision of PRED(.30) = 53.4%. Application/Improvements: This Paper Very Advanced Approach for Large Industrial Software Products.

A Novel Hybrid Algorithm for Software Cost Estimation Based on Cuckoo Optimization and K-Nearest Neighbors Algorithms

The inherent uncertainty to factors such as technology and creativity in evolving software development is a major challenge for the management of software projects. To address these challenges the project manager, in addition to examining the project progress, may cope with problems such as increased operating costs, lack of resources, and lack of implementation of key activities to better plan the project. Software Cost Estimation (SCE) models do not fully cover new approaches. And this lack of coverage is causing problems in the consumer and producer ends. In order to avoid these problems, many methods have already been proposed. Model-based methods are the most familiar solving technique. But it should be noted that model-based methods use a single formula and constant values, and these methods are not responsive to the increasing developments in the field of software engineering. Accordingly, researchers have tried to solve the problem of SCE using machine learning algorithms, data mining algorithms, and artificial neural networks. In this paper, a hybrid algorithm that combines COA-Cuckoo optimization and K-Nearest Neighbors (KNN) algorithms is used. The so-called composition algorithm runs on six different data sets and is evaluated based on eight evaluation criteria. The results show an improved accuracy of estimated cost.

A review of cost estimation models for determining assembly automation level

The aim of this research is to support early phase cost estimation for assembly systems design and automation decision. During this phase, various alternatives of assembly systems with different automation levels can be generated. The alternatives generation is performed using available information on product design, assembly sequences, and planned production information. The issue is to assess given alternatives, identify, and opt for the most appropriate one. Several criteria have to be considered in this decision. The economic aspect represents one of the most preponderant, including cost and profitability prediction. The importance and challenges of this complex issue are highlighted in this paper with feedbacks from manufacturers and the literature. The literature in the field is reviewed, presented, and analyzed. For this sake, a selection of cost models is performed covering a wide chronological range, journals, and fields including assembly and manufacturing models. Classification techniques of cost estimation works are presented and exploited in the proposed review. It is used to filter and discuss models suitability for the defined assembly automation decision issue. The most appropriate models are more thoroughly reviewed and discussed. Useful literature costing techniques, features, and relevant cost drivers are also identified. They cover multiple aspects as production information, resources features or performances. Finally, the review findings are illustrated by a cost estimation outline proposal to support early phase cost prediction for assembly systems design and automation decision.