Application of machine learning methods to cost estimation of product life cycle

Yeh, Tsung-Han; Deng, Sen

doi:10.1080/0951192x.2011.645381

Cited by 35 publications

(19 citation statements)

References 19 publications

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“…It is also emphasized in many studies that ANNs, which are frequently employed in engineering problems, yield a high performance (Apanavičienė ;and Juodis 2003;Berlin et al 2009;Gunduz et al 2011;Yeh and Deng 2012).…”

Section: Comparison Among Forecasting Modelsmentioning

confidence: 99%

Cost assessment of construction projects through neural networks

et al. 2013

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Construction performance parameters have experienced greater international attention and discussion in recent years. In this study, the change in the load-bearing system cost of a reinforced concrete housing estate building was investigated in relation to the building importance factor, earthquake region, soil type, floor area, and the number of stories. Three different housing estate projects with seven and fifteen stories were investigated. The structural design calculations were performed according to four different soil types, four different earthquake regions, and four different importance factors. With regards to each combination, project costs were calculated. The changes in the cost were examined with artificial neural network and multiple regression models. According to the results of the analysis, the building importance factor was the most significant component, while earthquake region was the second most significant, followed by the factors of the number of floors and the soil type.Résumé : Les critères de performance en construction ont fait l'objet d'une forte attention internationale et ils ont été des sujets de discussions au cours des dernières années. Dans la présente étude, le changement dans le coût du système porteur d'un ensemble résidentiel en béton armé est examiné par rapport au coefficient de risque de l'immeuble, à la région sismique, au type de sol, à la superficie de plancher et au nombre d'étages. Trois différents ensembles résidentiels de sept et de quinze étages ont été examinés. Les calculs de conception structurale ont été réalisés pour quatre types de sols différents, quatre régions sismiques et quatre coefficients de risque. Les coûts de projet ont été calculés pour chaque combinaison de facteurs. Les changements dans les coûts ont été examinés à l'aide de réseaux neuronaux artificiels et de modèles de régression multiple. Selon les résultats de l'analyse, le coefficient de risque de l'immeuble était la composante la plus importante, la seconde était la région sismique, suivie du nombre d'étages et du type de sol. [Traduit par la Rédaction] Mots-clés : estimation des coûts, réseau neuronal artificiel, région sismique, coefficient de risque de l'immeuble, type de sol, ensemble residential.

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Section: Comparison Among Forecasting Modelsmentioning

confidence: 99%

Cost assessment of construction projects through neural networks

et al. 2013

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“…The main purpose of network training is to minimize the network error. The criterion for stopping the training is the mean square error (MSE) which is calculated at the output layer [1]. If MSE reaches the set value, the training of the ANN model is terminated; otherwise, the weights between the output and hidden layers are reverse-transmitted and updated using the BP algorithm.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Therefore, having an accurate development cost prediction model to quickly formulate reasonable product sales price is extremely important for good decision-making and improving the competitiveness of companies. Although the cost of early manufacturing and design (development cost) of general aviation aircraft only accounts for a part of the life cycle cost (LCC), as proposed by Yeh and Deng [1], it determines the overall trend of LCC. However, due to the difficulty of data collection, late start time, multicollinearity between variables, the relevant cost prediction models are limited [2], and there is currently no specific model to forecast the general aviation aircraft cost.…”

Section: Introductionmentioning

confidence: 99%

“…The previous studies [8], [9] have demonstrated that the ANN method can obtain more accurate prediction performance owing to the deep learning ability of the model, which makes the ANN method not affected by the multi-collinearity of the cost data. The authors in [1] utilized the ANN method to predict product life cycle cost. The results indicated that the use of ANN method is feasible in the application of cost prediction of airframe structure manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

Chen

Huang

2020

IEEE Access

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The major objective of this paper is to build a cost prediction model for general aviation aircraft using artificial neural network (ANN) and principle component analysis (PCA) methods. A total number of 22 samples of general aviation aircraft collected from the literature are utilized to train and test the model. In the PCA, eigenvalues of PC1 and PC2 are 6.987 and 1.529, respectively, indicating that they have the strongest interpretation of the original variable information and are retained as cost influencing variables to train the ANN model. The pure multiple linear regression (MLR), stepwise regression (SR) and ANN models are built respectively for comparison. The comparative results reveal that the ANN method has better estimation effect than MLR and SR models in case of multi-collinearity of data. Combined with PCA, the ANN model is optimized, with MAPE, MAE, R and RMSE values of training and testing samples to be 0.009 and 0.015, 1.222 and 3, 0.9999 and 0.9994, 1.667 and 3.416, respectively. Finally, a more accurate and practical prediction model is developed. More importantly, this research can provide an important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategy.

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“…Also, BR is more desirable as its application is cost effective. Yeh and Deng [14] proposed a framework to predict the software product life cycle using two machine learning algorithms. The work presented a more precise and generalizable model for product cost estimation.…”

Section: Machine Learning Reviewmentioning

confidence: 99%

A Review of Machine Learning Models for Software Cost Estimation

Arslan

2019

Review of Computer Engineering Research

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Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. The aim of such a process is to have a better future sight of the project progress and its phases. Another main objective is to have clear project details and specifications to assist stakeholders in managing the project in terms of human resources, assets, software, data and even in the feasibility study. Accurate estimation results with definitely helps the project manager to do better estimation for the project cost, the time required for various project phases and resources or assets. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate. Contribution/Originality: This study contributes to the existing literature by enhancing the results of thirteen Machine Learning algorithms on two datasets. The evaluation criteria used in this work are R², MAE, RMAE, RAE, and RRSE. The aim of the proposed model is to predict the effort using dataset attributes and compare them with the actual effort in order to measure the error using different criteria.

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Application of machine learning methods to cost estimation of product life cycle

Cited by 35 publications

References 19 publications

Cost assessment of construction projects through neural networks

Cost assessment of construction projects through neural networks

Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

A Review of Machine Learning Models for Software Cost Estimation

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