Cost prediction method based on an improved fuzzy model

Kun, Zhai; Jiang, Nan; Pedrycz, Witold

doi:10.1007/s00170-012-4238-5

Cited by 13 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is important to note that expert's opinions are required initially to generate utility values of specific cost drivers considered as well defined, but in reality, they are not. Kirchain and Field (2000), Nikolaos and Lefteris (2013), and Zhai, Jiang, and Pedrycz (2013) suggested the need for looking at cost and/or process/material substitution not only at the individual part but also the whole system level. They called this the "Extrapolative Method" or "Total System Model" for evaluating cost effects.…”

Section: Cost Estimating Modelsmentioning

confidence: 98%

Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

Cai

Tyagi

2014

Journal of Cost Analysis and Parametrics

View full text Add to dashboard Cite

The research under product development domain identifies and implements new concepts in design, planning, manufacturing, testing, and service sectors to keep up with the market demands. Many product developments are multi-generational in nature and may require redesigning the product at each generation because an optimized strategy for a single generation may not be the best option in the multi-generation scenarios. With this regard, this article proposes a novel product life-cycle cost estimation model to support engineering decision-making in a multi-generational product development environment. This cost estimation model develops a mathematical formulation which has three major cost constituents: development cost, service cost, and associated risks cost. The risks cost further includes three components coming from technical risk, market risk, and operation risk. The developed mathematical formulation measures the on-going multi-generational product development's status, addresses its basic needs such as design for future reuse. It also serves as a strategic decision-making tool for successful project management. For the purpose of validating the proposed cost model, an empirical case study of a manufacturing-based product (gas turbine) is illustrated by considering three candidate concepts. This cost estimation is very handy to those design engineers who have a little idea of cost analysis when they design a product. It assists design teams by accurately forecasting and strategically planning a project and by making decisions which are compatible with future generations at a relatively cheaper cost. Thus, this article aims to provide a new direction and approach to maximize the business profit earned from multi-generational products.

show abstract

Section: Cost Estimating Modelsmentioning

confidence: 98%

Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

Cai

Tyagi

2014

Journal of Cost Analysis and Parametrics

View full text Add to dashboard Cite

show abstract

“…Therefore, a new trend evolves to solve this problem such as developing hybrid fuzzy modeling for the cost estimate purposes such as evolutionary-fuzzy modeling. Zhai et al, (2012) have created an improved fuzzy system which is established based on fuzzy c-means (FCM) to solve the problem of fuzzy rules generation. Zhu et al, (2010) have conducted an evolutionary fuzzy neural network model for cost estimation based on eighteen examples and two examples of training and testing, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Elmousalami

2021

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparison analysis for AI models is required to guide practitioners to the appropriate model. The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models, and ensemble methods such as scalable boosting trees (XGBoost). Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R 2 . Nonlinear adaptability, handling missing values and outliers, model interpretation and uncertainty have been discussed for the twenty developed AI models.

show abstract

“…Zhai et al. (2012) have created an improved fuzzy system which is established based on fuzzy c-means (FCM) to solve the problem of fuzzy rules generation.…”

Section: Fuzzy Modeling For Predictionmentioning

confidence: 99%

Intelligent methodology for project conceptual cost prediction

Elmousalami

2019

Heliyon

View full text Add to dashboard Cite

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Several methodologies exist to develop a conceptual cost model. However, many gaps exist in the current methodologies such as depending only on experts ‘opinions and questionnaire survey to identify the project features, key cost drivers and developing deterministic predictive models without taking uncertainty nature into consideration. The main contribution of this study is developing an intelligent methodology for predicting the project cost at the conceptual stage. The proposed methodology can automatically identify key cost drivers and maintain uncertainty to predicted cost. Field canals improvement projects (FCIPs) are used as a case study to validate the proposed methodology. The selected methodology has applied quantitative approaches to identify the key cost drivers. In addition, the methodology has applied a genetic fuzzy model that automatically generates fuzzy rules to automatically predict the conceptual cost. Moreover, the results show a superior performance of the genetic fuzzy model than the traditional fuzzy model. In addition, this study presents a publicly open dataset for FCIPs to be used for future models validation and analysis.

show abstract

Cost prediction method based on an improved fuzzy model

Cited by 13 publications

References 27 publications

Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Intelligent methodology for project conceptual cost prediction

Contact Info

Product

Resources

About