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In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused approach to manufacturing effectiveness. Selecting a proper AMS is a complicated task for the managers as it involves large tangible and intangible selection attributes. Failure to take right decision in selecting proper AMS alternative may even lead industry to losses. The purpose of this paper, therefore, is to rank the AMS alternatives by using fuzzy grey relational analysis, which will help managers when choosing an appropriate AMS.

This research proposes a multi‐attribute decision‐making (MADM) method, fuzzy grey relational analysis (FGRA), for AMS selection. The methodology is explained as follows. AMS alternatives and selection attributes will be chosen. The qualitative attributes will be converted into quantitative using fuzzy conversion scale. Then these data will be pre‐processed to normalize every value. This step is done to convert all alternatives into a comparability sequence. According to these sequences a reference sequence (ideal target sequence) is defined. Then, the grey relational coefficient between all comparability sequences and the reference sequence is calculated. Finally, based on these grey relational coefficients, the grey relational grade between the reference sequence and every comparability sequences is calculated. If a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the best choice. Fuzzy logic is used here to convert linguistic data into crisp score.

The proposed method is validated and compared by taking two examples from literature. The traditional statistical techniques require large data sets for evaluating attributes while grey theory on the contrary solve the multi attribute decision making problems with small data sets. This methodology will significantly increase the efficiency of decision making and overall competitiveness for manufacturing industries. This approach will motivate more and more industries to invest in AMS.

This method will help managers to weigh the AMS alternatives before actually buying them, which will in turn save money and time. This will build confidence of the top management for investing in costly technology such as AMS.

From time to time, various researchers have proposed various techniques to select the AMS. However, a survey on current evaluation methods shows that they are all less objective, lack accurate data processing, involve large calculations because of their complexity. In this paper, the authors attempt to solve the problem of AMS selection with FGRA, which is more logical, axiomatic, generates results in fewer steps with less calculations and is easy to understand. This paper succeeds in getting AMS alternatives' ranking using fuzzy grey relational analysis.

The purpose of this paper is to optimize the laser-assisted jet electrochemical machining parameters, namely, supply voltage, inter-electrode gap, duty cycle and electrolyte concentration during machining of WC-Co composite using grey relational analysis and fuzzy logic.

In this work, experiments were carried out as per the Taguchi methodology and an L16 orthogonal array was used to study the influence of various combinations of process parameters on material removal rate, hole taper angle and surface roughness height. As a dynamic approach, the multiple response optimization was carried out using grey relational analysis and fuzzy logic.

The process parameters were optimized using grey relational analysis and fuzzy logic for different machining conditions such as balanced manufacturing, high-speed manufacturing and high-quality manufacturing. The research documented in this paper can be scaled up for case studies regarding industrial applications to compare optimization methods for manufacturing processes that are already being carried out.

An attempt to optimize material removal rate, hole taper angle and surface roughness height together by a combined approach of grey relational analysis and fuzzy logic has not been previously done.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.

Supply chain performance (SCP) extent can be attributed as a function of multiple criteria/attributes. Most of the criterions/attributes being intangible in nature; SCP appraisement relies on the subjective judgment of the decision makers. Moreover, quantitative appraisement of SCP appears to be very difficult due to involvement of ill-defined (vague) performance measures as well as metrics. The purpose of this paper is to develop an efficient decision support system (DSS) to facilitate SCP appraisement, benchmarking and related decision making.

This study explores the concept of fuzzy logic in order to tackle incomplete and inconsistent subjective judgment of the decision makers’ whilst evaluating supply chain’s overall performance. Grey relational analysis has been adopted in the later stage to derive appropriate ranking of alternative companies/enterprises (in the same industry) in view of ongoing SCP extent.

In this work, a performance appraisement index system has been postulated to gather evaluation information (weights and ratings) in relation to SCP measures and metrics. Combining the concepts of fuzzy set theory, entropy, ideal and grey relation analysis, a fuzzy grey relation method for SCP benchmarking problem has been presented. First, triangular fuzzy numbers and linguistic evaluation information characterized by triangular fuzzy numbers have been used to evaluate the importance weights of all criteria and the superiority of all alternatives vs various criteria above the alternative level. Then, the concept of entropy has been utilized to solve the adjusted integration weight of all objective criteria above the alternative level. Moreover, using the concept of the grey ration grades, various alternatives have been ranked accordingly.

Finally, an empirical example of selecting most appropriate company has been used to demonstrate the ease of applicability of the aforesaid approach. The study results showed that this method appears to be an effective means for tackling multi-criteria decision-making problems in uncertain environments. Empirical data have been analysed and results obtained thereof, have been reported to exhibit application potential of the said fuzzy grey relation based DSS in appropriate situation.

The purpose of this paper is to develop an agility evaluation approach to determine the most suitable agile system for implementing mass customization (MC) strategies. Evaluating the alternatives and comparing across them, the best practices of the efficient organization can be identified and transferred to different organizations.

Grey relation approach is a simple mathematical technique useful in situations where the information is not known precisely. Grey relation approach has been applied to measure the agility of various organizations based on agile entities and accordingly the organizations are ranked. The ranking so obtained is compared with the ranking obtained by a popular multi‐attribute decision making (MADM) process known as Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) to test the robustness of the proposed method. It is to be noted that grey theory considers the condition of the fuzziness and can deal flexibly with the fuzziness situation.

It is demonstrated that the grey approach is an appropriate method for solving MADM problems in an uncertain situation with less computational efforts. The alternatives can easily be benchmarked and the best agile system can be selected. As the ranking obtained through grey relation approach closely agree with the ranking found from Fuzzy TOPSIS method, the robustness of the proposed approach is validated. Both the methods lead to choosing a suitable agile system related to mass customization.

In this paper, the proposed approach has been compared with Fuzzy TOPSIS method to test the robustness of the method. Other MADM approaches may be used for comparison purpose to gain insight into the methodology of the proposed approach.

An alternative approach for MADM is proposed to obtain good decisions in an uncertain environment and used for agility evaluation in selected organizations. As agile manufacturing is relatively a new concept, certain and complete information on systems are not available. In such situations, the proposed method can deal with the issue conveniently and results in workable solutions.

The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42.

The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (F_c) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L₁₈ mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method.

The optimum SR, cutting force (F_c) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics.

To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes.

In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance.

The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes.

This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values.

The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

The purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.

L27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.

It is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.

The influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers.

This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative.

The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach.

This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.