Cost Estimation Model Research Articles

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

AbstractOne of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta‐heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism‐based self‐adaptive mutation operator to solve the multi‐objective optimization problems. This approach addresses the issues that exist in multi‐objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism‐based self‐adaptive mutation operator in a multi‐objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non‐dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi‐objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi‐objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

Parametric cost estimation models of civil aircraft for the preliminary aircraft design phase

AbstractIn the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

Predicting the unpredictable: General Aviation (GA) aircraft cost estimation evaluation

Cost estimation is an important part of project planning as well as research endeavor. Since the well-established cost estimation models used in the GA aircraft industry are already several decades old, a re-evaluation of their applicability to current market conditions is essential. Reliable cost estimation may also improve the chances to get external funding - a vital point for start-ups. To tackle this issue, we developed a research method to investigate potential cost models for GA aircraft that can serve as a guideline for, e.g., start-ups and research works. After gathering existing cost estimation models, they are classified and analyzed to find the ones most suitable for small aircraft. For evaluation purpose, the two most promising ones are applied to data from existing aircraft models to compare their accuracy and finally the best one is coded as an application in Python to improve usability. With our presented research method we show a possibility to perform early cost estimation for small GA aircraft and offer a software tool to simplify its application.

Development of Conceptual Cost Estimation Model for an Economic Analysis of Steam Methane Reforming based Hydrogen Production

Development of Conceptual Cost Estimation Model for an Economic Analysis of Steam Methane Reforming based Hydrogen Production

Performance comparison of Putnam model using new technology trends for software maintenance cost estimation

Now the concept of software estimation is added by concrete concepts of fuzzy logic and neural networks that help in monitoring as well as understanding the software development dynamic process. The goal of all these is to estimate the estimating software effort. Approximate maintenance and judgment costs for a project is defined by software maintenance cost estimation. Due to environmental, human, political and technical like number of involved variables for cost estimate calculation, the maintenance cost estimation will never be same. In this paper we have proposed a new approach using Putnam model which is an efficient software maintenance cost estimation model. For dataset acquisition we have considered Bug Id reports instances of Matlab to perform classification of Sentimental based on severity. After pre-processing the dataset Principal Component Analysis (PCA) has been used to compute and study the Eigenvector for feature extraction. Then instance selection is performed using Particle Swarm Opitmization. In order to reduce matrix size there is need to deal with instances selection that will make processing easy to deal with further proceeding input. Once an instance is selected the next step is to classify it. In this work we have used Linear Discrement Analysis (LDA) as classifier which is an efficient statistical method used in machine learning. For testing the proposed approach we have compared the results obtained using COCOMO and PUTNAM model that shows the use of Putnam model will improve the system in terms of various parameters.

Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing

Industrial equipment/machinery is an important element of manufacturing. They are used for producing objects that people need for everyday use. Therefore, there is a challenge to adopt effective maintenance strategies to keep them well-functioning and well-maintained in production lines. This will save energy and materials and contribute genuinely to the circular economy and creating value. Remanufacturing or refurbishment is one of the strategies to extend life of such industrial equipment. The paper presents an initial framework of cost estimation model based on combination of activity-based costing (ABC) and human expertise to assist the decision-making on best life extension strategy (e.g. remanufacturing, refurbishment, repair) for industrial equipment. Firstly, ABC cost model is developed to calculate cost of life extension strategy to be used as a benchmark strategy. Next, expert opinions are employed to modify data of benchmark strategy, which is then used to estimate costs of other life extension strategies. The developed cost model has been implemented in VBA-based Excel® platform. A case study with application examples has been used to demonstrate the results of the initial cost model developed and its applicability in estimating and analysing cost of applying life extension strategy for industrial equipment. Finally, conclusions on the developed cost model have been reported.

Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

An enhanced support vector regression model for agile projects cost estimation

The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.

The cost of testicular torsion care in Ireland 2009-2018: a novel cost estimation model using a national database.

Activity-based funding (ABF) is a reimbursement model for public hospitals in Ireland. Little data exist regarding cost estimates for acute surgical admissions in Ireland. This study presents a novel method of direct cost estimation for testicular torsion (TT) care in Ireland. Hospital Inpatient Enquiry (HIPE) data, covering all public hospital admissions in Ireland, were analysed to derive annual incidence rates for TT between 2009 and 2018. The monetary cost of each case was calculated by cross-referencing the diagnosis-related group (DRG) with reference prices for inpatients and day-cases in public hospitals in 2019. Annual cost was adjusted using the Consumer Price Index for Health (Ireland). One thousand seven hundred forty-six patients under 25years underwent orchidectomy or orchidopexy for TT between 2009 and 2018. The direct cost of TT care in public hospitals between 2009 and 2018 was €6,331,402. Costs increased 54% over 10years, from €513,232 in 2009 to €788,700 in 2018 (2019 Euros). Just over two-thirds of cases (70%, n = 1230) were reimbursed with public funding. This novel cost estimation model may serve as a template for future direct cost estimates for surgical interventions in Ireland. This will improve the accuracy of future economic evaluation for healthcare interventions in Ireland.

Construction of a new levelled cost model for energy storage based on LCOE and learning curve

New energy storage is essential to the realization of the “dual carbon” goal and the new power system with new energy as the main body, but its cost is relatively high and the economy is poor at present. This paper studies the levelized cost of new energy storage based on the whole life cycle perspective. Based on LCOE and learning curve methods, a new levelled cost estimation model and prediction model for energy storage are constructed. Based on the latest development status of electrochemical new energy storage, the levelized cost of energy of lithium-ion batteries, flow-aluminum batteries, and flow-zinc batteries were measured, the cost composition and proportion of various types of energy storage are analyzed, and on this basis, the levelized cost of lithium-ion batteries was predicted. Comparative analysis shows that the levelized cost per kilowatt-hour of lithium-ion batteries is the lowest. This article provides a certain reference for the construction and layout of energy storage on three sides of the source network and load.

F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.

Cost Modeling from the Contractor Perspective: Application to Residential and Office Buildings

For the majority of the contractual arrangements used in construction projects, the owner is not responsible for the cost deviations due to the variability of labor productivity or material price, amongst many other aspects. Consequently, the cost performance of a project may be entirely distinct for the owner and the contractor. Since the majority of quantitative research on cost estimation and deviation found in the literature adopts the owners’ perspective, this research provides a contribution towards modeling costs and cost deviation from a contractor’s perspective. From an initial sample of 13 residential buildings and 10 office building projects, it was possible to develop models for cost estimation at the early stage of development, including both endogenous and exogenous variables. Although the sample is relatively small, the authors were able to fully analyze all the cost data, using no secondary sources of data (which is very frequent in cost modeling studies). The statistically significant variables in the cost estimation models were the areas above and below ground and the years following the 2008 financial crisis, including the international bailout (2011–2014) period. For estimating the unit cost, a nonlinear model was obtained with the number of underground and total floor, the floor ratio, and the years following the 2008 financial crisis, including the international bailout (2011–2014) period as predictors. For the office buildings, a statistically significant correlation was also found between the cost deviation and number of underground floors.

Development of open-pit mine reclamation cost estimation models: A regression-based approach

In the recent decade, very few studies have been done on mine reclamation cost estimation and no study has been conducted on proposing mine reclamation cost estimation models based on historical data. This study aims to develop predictor models for mine reclamation costs. To this end, after collecting the historical cost data of 41 open-pit mine reclamation projects, a comprehensive data set of 16 mine reclamation costs groups and the extent of the disturbed mined land corresponding to each group was prepared. Given the advantage of the regression method in developing a reliable predictor model with few data, the proposed cost models are developed based on the regression analysis technique. The R square for all and more than 87% of the developed models was more significant than 85% and 90%, respectively, indicating the proper fits on the data sets. Also, the root mean square error ratio to the standard deviation of observed cost data (RSR) was lower than 0.7 for all developed models, indicating the predictor models' good performance on reliably estimating mine reclamation costs. These efficient and simple general models can help make the right decisions by mine reclamation planners and pave the way to achieve sustainable mining by considering mine reclamation cost in the mine planning and design process.

The Analysis of Cost Estimation using Cost Significant Model on Bridge Construction in South Sulawesi

One of the fundamental problems faced by the province of South Sulawesi is the factor of accessibility, so the role of bridges is quite important. For this reason, the budget planning for standard bridge construction projects also needs to be efficient in terms of preparation and accurate in terms of budget. The Cost Significant Model is one of the total construction cost estimation models that relies more on the prices that have the most influence on the total project cost as the basis for estimation. In general, this study uses data from steel frame bridge construction projects in South Sulawesi Province to formulate a mathematical model with linear regression analysis so that it can be used in the process of estimating similar projects going forward. The Estimation Model which is formed from the regression analysis and the Cost Significant Model in this study, namely; Y = 3.884 (X7) + 0.989 (X8) - 65515.372. With; Y = Estimated Total Cost (Rp/m); X7 = Reinforcement Work Cost (Rp/m); X8 = Steel Frame Structure Work Cost (Rp/m). Where this model can explain 99.7% of the total project cost with a cost model factor of 1.038. The level of accuracy (percentage error estimate) of the estimation results of the Cost Significant Model in this study ranges from - 1.46% to +2.45%.

Techno-Economic Assessment of Fuel Cycle Facility of System Integrated Modular Advanced Reactor (SMART)

The economic assessment of advanced nuclear power reactors is very important, specifically during the early stages of concept design. Therefore, a study was performed to calculate the total cost estimation of fuel cycle supply for a system modular advanced reactor (SMART) by using the Generation-IV economic program called G4-ECONS (Generation 4 Excel-based Calculation of Nuclear Systems). In this study, the detailed description of each model and methodology are presented including facility, operations, construction matrix, post-production model, and fuel cycle cost estimation model. Based on these models, six Generation-III+ and Generation-IV nuclear reactors were simulated, namely System 80+ with benchmark data, System 80+ with uranium oxide (UOx) and mixed oxide (MOx) fuel assemblies, fast reactor, PBMR (Pebble Bed Modular Reactor), and PWR (Pressurized Water Reactor), with partially closed and benchmarked cases. The total levelized costs of these reactors were obtained, and it was observed that PBMR showed the lowest cost. The research was extended to work on the SMART reactor to calculate the total levelized fuel cycle cost, capital cost, capital component cost, fraction of capital spent, and sine curve spent pattern. To date, no work is being reported to calculate these parameters for the SMART reactor. It was observed that SMART is the most cost-effective reactor system among other proven and advanced pressurized water-based reactor systems. The main objective of the research is to verify and validate the G4-ECONS model to be used for other innovative nuclear reactors.

Machine Learning Modeling of Forest Road Construction Costs

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.

Earthquake Damage Repair Loss Estimation in New Zealand: What Other Variables Are Essential Based on Experts’ Opinions?

Major earthquakes can cause extensive damage to buildings and alter both the natural and built environments. Accurately estimating the financial impact from these events is complex, and the damage is not always visible to the naked eye. PACT, SLAT, and HAZUS are some of the computer-based tools designed to predict probable damage before an earthquake. However, there are no identifiable models built for post-earthquake use. This paper focuses on verifying the significance and usage of variables that specifically need to be considered for the post-earthquake cost estimation of earthquake damage repair work (CEEDRW). The research was conducted using a questionnaire survey involving 92 participants who have experience in cost estimating earthquake damage repair work in New Zealand. The Weighted Average, Relative Importance Index (RII), and Exploratory Factor Analysis were used to analyse the data. The research verified that eleven major variables that are significant to the CEEDRW and should be incorporated to cost estimation models. Verified variables can be used to develop a post-earthquake repair cost estimation tool and can be used to improve the pre-earthquake loss prediction tools.

Utilization and Cost Estimation Models for Highway Fleet Equipment

Highway agencies need to manage the utilization of their highway equipment assets to reduce fleet management costs, balance equipment use, and provide the required services. Predictive equipment utilization and operational cost models are required for optimal management; however, there are no widely accepted models for this purpose. Although the utilization data is collected by state DOTs, the literature does not show any specific statistical model to predict equipment utilization as a function of contributing factors such as asset age, fleet size, costs, and demand for service. This study will bridge this gap and develop a predictive model to estimate the utilization of fleet equipment. The main objective of this paper is to develop a set of predictive models to estimate the annual utilization of seven non-stationary highway equipment types based on several explanatory variables including their annual fuel cost, downtime hours, age, and weight. Furthermore, another set of models are fit to predict the annual operational cost for these equipment types based on the most important contributing factors. The prediction models are developed after a nationwide data collection. Several years of collected data from seven states are processed and used for model development. This research has identified annual mileage as an appropriate and widely used utilization metric. Various model structures to predict annual mileage are considered. The logarithmic function of annual mileage has provided the most appropriate structure. The final annual mileage predictive models have R-squared values that are between 0.65 and 0.89, which indicates a good fit for all models. The models are validated by performing several statistical tests and they have satisfied all required assumptions of regression analysis. The result of modeling and statistical analysis showed that the proposed models accurately estimated the utilization and operational cost for highway equipment assets.

Methodology for the Concept Design of Locally Reinforced Composites

Locally load-optimized fiber-based composites, the so-called tailored textiles (TT), offer the potential to reduce weight and cost compared to conventional fiber-reinforced plastics (FRP). However, the design of TT has a higher complexity compared to FRP. Current approaches, focusing on solving this complexity for multiple objectives (cost, weight, stiffness), require great effort and calculation time, which makes them unsuitable for serial applications. Therefore, in this paper, an approach for the efficient creation of simplified TT concept designs is presented. By combining simplified models for structural design and cost estimation, the most promising concepts, regarding the cost, weight, and stiffness of TT parts, can be identified. By performing a parameter study, the cost, weight, and stiffness optima of a sample part compared to a conventional FRP component can be determined. The cost and weight were reduced by 30% for the same stiffness. Applying this approach at an early stage of product development reduces the initial complexity of the subsequent detailed engineering design, e.g., by applying methods from the state of the art.

MODEL ESTIMASI BIAYA DENGAN COST SIGNIFICANT MODEL DAN ARTIFICIAL NEURAL NETWORK PROYEK PENINGKATAN JALAN ASPAL DI YOGYAKARTA

The need for an analytical method that may provide an accurate project cost estimation with minimal data availability becomes very necessary. Therefore, this research was conducted to determine and compare the cost estimation model based on the Cost Significant Model (CSM) and Artificial Neural Network (ANN) with two modeling approaches, ANN-1 and ANN-2. The models were developed based on 28 data of road improvement projects in Yogyakarta from the year 2010 until 2019. The analysis results show that the ANN-2 provides the best validation compared to the ANN-1 and the CSM model. The value of Mean Absolute Percentage Error (MAPE) of ANN-1 with the 3-8-1 net scheme provides a value of 12.687%, while that of ANN-2 with 10-15-1 net scheme is 8,132% and the MAPE value of the CSM model produces a value of 14.757%.