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Evaluation of pollution prevention options in the municipal water cycle
Published in Alberto Galvis Castaño, Integrated Pollution Prevention and Control for the Municipal Water Cycle in a River Basin Context, 2019
Water management is typically a multi-objective problem which makes multicriteria decision analysis MCDA a well-suited decision support tool (Hajkowicz and Collins, 2007). There is no single multi-criteria decision analysis MCDA method that can claim to be a superior method for all decision (Mutikanga et al., 2011). Whilst selection of the MCDA technique is important more emphasis is need on the initial structuring of the decision problem, which involves choosing criteria and decision options (Hajkowicz and Higgins, 2008). The wastewater treatment alternative selection is a MCDA, where uncertainty, complexity and hierarchy need to be considered. (Zeng et al., 2007) propose a multi-criteria analysis methodology including: AHP and (GRA). AHP is useful for handling multiple criteria and objectives in the decisionmaking process. The GRA is a measurement method in grey system theory that analyzes uncertain relations between one main factor and all the other factors in a given system (Liu et al., 2005; Tosun and Pihtili, 2010). The hierarchy GRA combines the traditional GRA with the idea of the hierarchy of the AHP. It enables a more effective evaluation than just the mono level-based evaluation. The different levels of importance of the criteria are reflected through weighting factors to avoid subjectivity and randomness. In addition, the quantified evaluating scale, namely the integrated grey relational grade, makes the wastewater treatment alternative selection more comparable and comprehensive. Grey system theory was developed by Deng (1982) and has been successfully applied in engineering prediction and control, social and economic system management, and environmental system decision making in recent years.
A Holistic Grey-MCDM Approach for Green Supplier Elicitation in Responsible Manufacturing
Published in Ammar Y. Alqahtani, Elif Kongar, Kishore K. Pochampally, Surendra M. Gupta, Responsible Manufacturing, 2019
Gazi Murat Duman, Elif Kongar, Surendra M. Gupta
Grey systems are gaining popularity in the literature due to their ability to cope with uncertainty. In their literature survey, Tozanli et al. [10] stated that the total number of fuzzy articles incorporating grey theory has increased significantly over the past 5 years. In this study, the decision makers found grey system theory to be a better fit, considering its various advantages, and used grey numbers for the ratings of attributes that were initially expressed using linguistic variables.
A diagnosis method for diesel engine wear fault based on grey rough set and SOM neural network
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
Silin Qian, Shenghan Zhou, Wenbing Chang, Yiyong Xiao, Fajie Wei
Grey system theory is one of the important methods and techniques for studying uncertain systems. And grey relational analysis is a very active branch in the grey system theory, which basic idea is to divide the factors as sequence curve, and then through the similarity degree of geometric shapes to obtain the correlation degree of each factors (Gao et al. 2013). The closer the shape of the curve is, the greater the correlation of the corresponding sequence is determined.
The grey Theta forecasting model and its application to forecast primary energy consumption in major industrial countries
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Different from the energy demand prediction algorithms mentioned above, grey system theory is often used for modeling analysis when the system state is unstable or available data is limited. Grey system theory is mainly aimed at the modeling of small sample uncertain information, so it is widely used in engineering application and market economy prediction research. There are many improvements and applications in traditional grey model (GM(1,1)). Xie and Liu (Xie and Liu 2009) proposed a discrete grey model, which simplifies the process of modeling and calculation. This is an important improvement to avoid systematic errors when differentiating (Wang and Li 2019). Xiao et al. (2017) proposed a new type of seasonal grey model and used rolling prediction to predict traffic flow. Zeng, Meng, and Tong (2016) proposed a grey prediction model with an intelligent structure. Wu et al. (2013a) discussed the modeling defects of the first-order cumulative grey model and proved that the grey model is only suitable for small sample modeling. Based on this, the fractional derivative has attracted a lot of attention in the application of grey model. Wu et al. (2013b) first gave the modeling formula of non-integer difference in the grey model according to the “in between” principle. Then, conformable fractional difference (Ma et al. 2020; Xie et al. 2020) and Harsdorf fractional difference operation (Chen et al. 2020) complement the traditional fractional grey model theory, which greatly simplifies the calculation process of the model.
A model of BIM application capability evaluation for Chinese construction enterprises based on interval grey clustering analysis
Published in Journal of Asian Architecture and Building Engineering, 2021
Ailing Wang, Mengqi Su, Shaonan Sun, Yuqin Zhao
In terms of research methods of BIM application capability evaluation, most literatures utilized analytic hierarchy process (AHP) for evaluation (Wang et al. 2017; Yu 2017). Although AHP is a common method to solve multicriteria decision-making (MCDM) problems, the results obtained through it tend to be subjective. Moreover, capability assessment is affected by many factors, and most of these factors are qualitative measures, which makes it difficult to conduct quantitative evaluation when the information is uncertain and incomplete. Grey system theory is a method to handle uncertainties in small data samples with imprecise information (Wong and Hu 2013; Sun, Liang, and Wang 2019). Grey clustering analysis is one of the classic methods of grey evaluation methods. It is the combination of grey system theory and cluster analysis, and is widely utilized in many fields such as economics, military, biology, transportation and environmental quality assessment (Yuan and Liu 2012; Pei and Wang 2013; Xie, Liu, and Zhan 2013; Jian et al. 2014; Shen, Xu, and Wang 2008; Wang, Ning, and Chen 2012; Li, Zhang, and He 2012; Jia, Mi, and Zhang 2013). The traditional grey clustering analysis is generally based on the real number domain, which is not applicable when the sample value is an interval. Considering this problem, the researchers proposed interval grey clustering analysis (IGCA) (Zhou et al. 2013; Wang et al. 2015; Qian, Liu, and Xie 2016; Dang et al. 2017). However, the reported applications of this method in the construction industry are limited.
Simplified likelihood estimation of ship total loss using GRA and CRITIC methods
Published in Transportation Planning and Technology, 2020
Tianyu Xu, Xiaojing Liu, Zeling Zhang
Initiated in 1982, grey system theory can be applied to analyse uncertain systems with incomplete or uncertain information or incomplete data (Tosun 2006; Liu, Yang, and Forrest 2017; Sun et al. 2018). As a part of grey system theory (Jiang and He 2012), grey relational analysis (GRA) has been widely used for analysing statistical data across multiple research fields. For example, it has been used to explore the correlation between energy consumption and GDP (Kose, Burmaoglu, and Kabak 2013), examine workplace safety (Ai, Hu, and Chen 2014), discover key influencing factors of executive compensation (Chen, Zhang, and Liu 2016), investigate correlations between air quality and cities (Fu, Gao, and Wu 2018), and select the safety evaluation index of civil aircraft (Su and Xie 2018).